Researchers used ML to detect PMUs

A new research collaboration between China and the US offers a way of detecting malicious ecommerce reviews designed to undermine competitors or facilitate blackmail by leveraging the signature behavior of such reviewers.

Machine learning algorithm has managed to detect PMUs

The paper describes how a system called the malicious user detection model (MMD) analyzes the output of such users to determine and label them as Professional Malicious Users (PMUs). Using Metric Learning, a method used in computer vision and recommendation systems, and a Recurrent Neural Network (RNN), the system identifies and categorizes the output of these critics.

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
The ML algorithm was able to detect PMUs

User experience may be evaluated using star ratings (or a score out of ten) and text-based comments, which usually make sense in a typical scenario. PMUs, on the other hand, frequently go against this thinking by submitting a negative text evaluation with a high rating or a poor rating with a good review.

It’s a lot more pernicious because it allows the user’s review to inflict reputational harm without setting off e-commerce sites’ rather simple filters for identifying and addressing maliciously bad comments. If an NLP filter detects invective in a review, the high star (or decimal) rating assigned by the PMU effectively cancels out the negative content, making it seem ‘neutral,’ statistically speaking.

The new study states that PMUs are often used to demand money from internet retailers in exchange for amending negative comments and a promise not to post any more bad reviews. Some individuals seeking discounts are sometimes employed by the victim’s rivals, albeit most of the time, the PMU is being unethically utilized by the victim’s competitors.

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
There are no comparable prior works that can detect PMUs.

The newest variety of automated detectors for such examinations employs Content-Based Filtering or a Collaborative Filtering approach, seeking unequivocal ‘outliers’. These are dismal negative reviews in both feedback modes and differ significantly from the overall trend of review sentiment and rating.

A high posting frequency is a typical sign that such filters look for. In contrast, a PMU will post strategically but seldom since each review may be an individual commission or a component of a longer plan to obscure the ‘frequency’ statistic.

Because of this, the paper’s researchers have incorporated the unusual polarity of expert malicious comments into a separate algorithm, giving it nearly identical capabilities to a human reviewer in detecting fraudulent reviews.

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
For the first time, it was possible to detect PMUs using this method.

Previous studies

According to the authors, there are no comparable prior works to compare MMD against because it is the first technology to try to detect PMUs based on their schizophrenic posting style. As a result, the researchers compared their method against various component algorithms previously used by conventional automatic filters, including; HysadSemi-sadStatistic Outlier Detection (SOD); K-means++ Clustering; CNN-sad; and Slanderous user Detection Recommender System (SDRS).

“[On] all four datasets, our proposed model MMD (MLC+MUP) outperforms all the baselines in terms of F-score. Note that MMD is a combination of MLC and MUP, which ensures its superiority over supervised and unsupervised models, ” the researchers said.

The paper further states that MMD could be used as a pre-processing method for standard automatic filtering systems, and it presents experimental results on several datasets such as User-based Collaborative Filtering (UBCF), Item-based Collaborative Filtering (IBCF), Matrix Factorization (MF-eALS), Bayesian Personalized Ranking (MF-BPR), and Neural Collaborative Filtering (NCF).

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
The MMD is a generic solution that can detect PMUs.

According to the article’s conclusions, the authors say that in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), these investigated augmentations resulted in improved results:

“Among all four datasets, MMD improves the recommendation models in terms of HR and NDCG. Specifically, MMD can enhance the performance of HR by 28.7% on average and HDCG by 17.3% on average. By deleting professional malicious users, MMD can improve the quality of datasets. Without these professional malicious users’ fake [feedback], the dataset becomes more [intuitive].”

The paper is called Professional Malicious User Detection in Metric Learning Recommendation Systems and was published by researchers at Jilin University’s Department of Computer Science and Technology; the Key Lab of Intelligent Information Processing of China Academy of Sciences in Beijing; and Rutgers University’s School of Business.

Method

It is hard to detect PMUs because two non-equivalent parameters (a numerical-value star/decimal rating and a text-based review) must be considered. According to the new paper’s authors, no similar research has been done before.

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
HDAN uses the emphasis to assign weights to each word and each sentence.

The review subject is divided into content chunks using a Hierarchical Dual-Attention recurrent Neural network (HDAN). HDAN uses the emphasis to assign weights to each word and each sentence. In the picture above, the authors state that the term “poorer” should be given greater importance than other words in the review.

The MMD algorithm uses Metric Learning to estimate an exact distance between items to characterize the entire set of connections in the data.

MMD uses a Latent Factor Model (LFM) to select the user and item, which gets a base rating score. HDAN, on the other hand, incorporates reviews into the sentiment score as supplementary information.

The MUP model generates the sentiment gap vector, which is the difference between the rating and the predicted sentiment score of the review’s text content. For the first time, it was possible to detect PMUs using this method.

The output labels are used in Metric Learning for Clustering (MLC) to establish a metric against which the probability of a user review being malicious is calculated.

Researchers From China And The Us Developed An Ml Model That Is Able To Detect Pmus (Professional Malicious Users) That Publish Fake Negative Reviews.
On average, the students identified 24 true positives and 24 false negatives out of a 50/50 mix of good and bad reviews.

The researchers also performed a user study to see how effectively the system identified malicious reviews based only on their content and star rating. The participants were asked to assign the evaluations a score of 0 (for ordinary users) or 1 (for an experienced malevolent user).

On average, the students identified 24 true positives and 24 false negatives out of a 50/50 mix of good and bad reviews. MMD was able to label 23 genuine positive users and 24 genuine negative users on average, operating almost at human levels, surpassing the task’s baseline rates.

“In essence, MMD is a generic solution that can detect the professional malicious users that are explored in this paper and serve as a general foundation for malicious user detections. With more data, such as image, video, or sound, the idea of MMD can be instructive to detect the sentiment gap between their title and content, which has a bright future to counter different masking strategies in different applications,” the authors explained. If you are into ML systems, check out the history of Machine Learning, it dates back to the 17th century.


Source link

Most wanted business intelligence analyst skills (2022)

We gathered all of the essential business intelligence analyst skills for you. If you want your company to stay successful and increase brand recognition far beyond the competition, you need BI. Keeping this in mind, jobs like Business Intelligence Analyst are becoming increasingly popular! That’s why you come in! You can take advantage of the growing popularity of Business Intelligence to get a BI Analyst job that will teach you a lot while also taking your career to new heights. Cloud computing jobs are also on the rise. We have already explained cloud computing job requirements, trends, and more in this article. But what abilities are necessary for a Business Intelligence Analyst?

What are the business intelligence analyst skills needed for a successful career?

Business Intelligence Analysts analyze data to help firms improve their earnings by keeping track of current market trends. Analysts have access to a variety of data. A company’s database, web crawling software, or going to another firm’s data and checking in may all supply information. The collected data is then used to construct a picture of the present market and the best route for a business to follow in the future.

Be A Part Of The Data-Driven Culture As A Business Intelligence Analyst
Business intelligence analyst skills (2022)

Using data to meet organizational goals puts Business Intelligence (BI) in the limelight.

BI is a broad term that includes the operation and management of data processing tools and systems, such as data visualization tools, data modeling tools, decision-support systems, database management systems, and data warehousing systems.

A bachelor’s degree in business, management, accounting, economics, statistics, information science, or a closely related discipline is generally required for BI analysts. For higher-level or high-profile employment, more advanced degrees are required. This sector deals with a lot of data, a changing market, and predictions. Having excellent analytical, organizational, and forecasting abilities in this industry is quite advantageous.

What are the business intelligence analyst’s jobs?

The business intelligence analyst assesses both company data and data from rivals and others in the industry to discover methods to improve the company’s market position in today’s data science workforce. Analysts with a good understanding of the company’s systems, procedures, and functions will evaluate their firm’s operations, processes, and functions to identify where it may improve efficiency and profit margins.

Business intelligence analysts should also consider new methods to create new policies around data gathering and analysis techniques and ensure data use. At times, business intelligence analysts may be tasked with employing other data experts, such as data architects. Business intelligence creates collaboration in the workforce. We already explained how business intelligence creates collaboration in this article.

What are the business intelligence analyst qualifications?

A BI Analyst must possess a wide range of abilities. Here are some examples:

Data preparation

The purpose of data preparation is to allow you to extract useful information from your data. Firstly, to get any insights from the data, the data must be gathered, cleaned, and arranged consistently. Using many data preparation tools, you may collect data from several sources and then transform it into the same dimensions and measurements. And as a BI Analyst, you should be conversant with at least some of these data preparation technologies, such as Tableau Prep, Improvado, Alteryx, etc.

Business intelligence analyst education requirements

It is one of the most terrifying business intelligence analyst skills. The most popular requirement for BI analysts is a bachelor’s degree. To acquire the skills you’ll need on the job, consider majoring in statistics, business administration, computer science, or a closely related discipline. Some BI experts pursue an MBA or a data science graduate degree.

Be A Part Of The Data-Driven Culture As A Business Intelligence Analyst
Business intelligence analyst skills (2022): Business intelligence analyst education requirements

Data tools

Business intelligence analysts use various technologies to access, analyze, and visualize data. They might require knowledge of Structured Query Language, or SQL, a tool for retrieving data from databases. Other tools that business intelligence analysts may utilize are Tableau and Power BI, which allow them to pull information from data sources and generate visualizations like graphs.

Data mining

The technique of data analysis is known as data mining. This transforms raw data into useful information that may be utilized to make decisions. The subject of data mining necessitates an understanding of various technologies, including machine learning, databases, statistical analysis, computer science algorithms, etc. The Rapid Miner, Oracle Data Mining, Konstanz Information Miner, and others are some of the tools that are particularly valuable for data mining. It is one of the most important business intelligence analyst skills.

Data analysis and modeling

The ability to comprehend and convert data into insights is a must. The BI BA must be able to think conceptually and employ high-level data models to map the real world of the organization conceptually. They also need a clear knowledge of how data travels from operational source systems throughout the company, through various transformation techniques, and where decision-makers utilize it.

Industry knowledge

Data analysis is only as good as the data you’ve gathered from your company’s system. If you want to give useful reports that provide practical insights, you must understand the basics and intricacies of your industry. This implies looking beyond just focusing on your company’s goals and objectives but also understanding the various KPIs required in every field.

It would be best if you also stayed up to speed on the latest developments in business intelligence. You may do this by reading the news every day. If you want to go even deeper, consider taking training courses about what’s new in the field or even subscribing to reports and surveys from major players in the sector.

Data visualization

As a BI analyst, presenting the data is essential to your work. Data visualization skills are crucial for people aiming to be Business Intelligence Analysts. It would help if you understood the different charts that may represent the data, such as Area Charts, Bar Charts, Heat Maps, TreeMaps, Scatter Plots, Gantt Charts, and so on. These charts allow decision-makers to better understand the data by displaying it and seeing how it evolves gradually. It is one of the most crucial business intelligence analyst skills.

Be A Part Of The Data-Driven Culture As A Business Intelligence Analyst
Business intelligence analyst skills (2022): Data visualization

Communication

The objective of business intelligence analysts is to turn raw data into information that others can grasp. They must be capable of describing data, explaining their interpretation of it, and outlining actions the firm may undertake due to the analysis. Translating complex technical matters to those unfamiliar with the terminology and processes involved may be a part of this procedure.

Problem-solving

The job of a business intelligence analyst is to interpret data to discover problem areas, then propose solutions based on that data. The responsibilities include devising practical recommendations for improving operations and influencing better decision-making.

Business acumen

Knowing how to use business intelligence software and analyze data isn’t enough to increase the effectiveness of a company’s analysis. You’ll also need an understanding of the firm you’re working for it. This will allow you to conduct analyses in line with the company’s goals.

To develop your business acumen, you must study the company’s business model, grasp its short- and long-term objectives, pinpoint its critical problems, and identify its major rivals. The goal is to think about an executive and operational level so that you can use data more effectively and have a detailed approach to decision-making.

Critical thinking

For business intelligence analysts, critical thinking is a must. It simply implies the ability to methodically examine data to understand ideas at a deeper level and find practical applications for them. This involves asking yourself — do you really need business intelligence software?

In terms of using BI, critical thinking will allow you to identify anomalies in data, assess their impact on the business, and propose effective solutions to issues you encounter. It’s also important to assist you in evaluating your work so that you may improve it later.

Programming

A business intelligence analyst might find programming helpful since it allows them to create scripts or sets of instructions that may automate data-related activities like finding and changing specific data. This can help them organize their tasks and speed up their workflow. Business intelligence analysis frequently utilizes SQL and Python, a sophisticated programming language.

Data reporting

Knowing how to structure and present a technical report, either in Microsoft Excel or HTML, is a must-have. Soft skills such as communication and reporting findings from data are critical for your job as a business intelligence analyst. You should be able to communicate the insights obtained from the data to senior management at the company, such as stakeholders and board members, to make important decisions based on them. It’s also crucial to remember that most decision-makers tend to be technical specialists, so it’s critical to make clear technical ideas using simple language.

Business intelligence skills for resume

Here’s a list of job-related BI skills you may use in resumes, cover letters, job applications, and interviews. There are the most wondered business intelligence analyst skills.

Be A Part Of The Data-Driven Culture As A Business Intelligence Analyst
Business intelligence analyst skills (2022): Skills for resume

The required abilities will differ depending on the position for which you’re applying, so be sure to examine other lists of abilities.

  • Adapting to changing priorities
  • Assessing client/end-user needs
  • Attention to detail
  • Business strategies
  • C/C++
  • Client relations
  • Coaching
  • Coding
  • Collaboration
  • Computer science
  • Consulting
  • Coping with deadline pressure
  • Creating reports
  • Creating and running what-if simulations
  • Data architecture
  • Data controls
  • Data management
  • Data modeling
  • Data visualization
  • Debugging data output irregularities 
  • Defining data access methods
  • Delegating
  • Designing enterprise-level reporting
  • Designing/modifying data warehouses
  • Evaluating business intelligence software
  • Extract, transform, load (ETL) testing
  • Facilitating the creation of new data-reporting models
  • Finding trends/patterns
  • IBM Cognos Analytics
  • Innovation
  • Insights
  • Java
  • Leading cross-functional teams
  • Maintaining technical documentation for solutions
  • Managing relationships with vendors
  • Managing stress
  • MatLab
  • Mentoring
  • Microsoft Excel
  • Microsoft Integration Services
  • Microsoft Office
  • Microsoft Power BI
  • Modeling
  • Monitoring data quality
  • Motivating staff
  • Multitasking
  • Negotiating
  • Online analytical processing (OLAP)
  • Organizational approach
  • Programming
  • Python
  • Reporting tools
  • Researching solutions to user problems
  • Results-oriented
  • SAS
  • Statistical analysis
  • Statistical knowledge
  • Strategic thinking
  • Time management
  • Training end-users
  • Translating high-level design into specific implementation steps
  • Web analytic tools

Entry-level business intelligence analyst salary and more: Business intelligence analyst salary in 2022

A business intelligence analyst is a skilled professional in data analysis who may earn up to almost $100,000 per year. A job in this field, which has been named one of the hottest jobs in the STEM (Science, Technology, Engineering, and Mathematics) field by American business magazine Forbes, is highly sought after due to the high demand from a variety of sectors including finance, healthcare, manufacturing, insurance, technology, and e-commerce.

Be A Part Of The Data-Driven Culture As A Business Intelligence Analyst
Business intelligence analyst skills (2022): Salaries

According to PayScale statistics, the yearly pay for a business intelligence analyst ranges from $48,701 to $93,243 in the United States, with a standard salary of $66,645 per year. You can find detailed information about salaries below.

Role Salary
Data analyst $65,981
Business analyst $75,339
Product analyst $76,864
Business intelligence consultant $91,517
Senior business intelligence analyst $103,055
Business intelligence architect $112,049
Business intelligence manager $116,684

Do you have the skills and qualifications needed to work as a BI analyst? What do you think is the most important feature for a BI analyst? Please share your opinions in the comments.


Source link

Social computing: Definition, types, examples, and more

Social computing is a branch of computer science that studies how people interact with computers and computational systems. Computing is inherently a social activity. Networks connect people for research, education, commerce, and entertainment. Today, a computer that is not connected to the internet appears to be broken—what can we do with it? Social Computing is concerned with how to make technology more person-centered.

What is social computing?

The social and interactive aspect of online activity is known as social computing. The phrase may be interpreted in contrast to personal computing, which refers to the activities of single users.

Blogs, wikis, Twitter, RSS, instant messaging, multi-gaming, and open source development are just a few examples of social computing. It also includes social networking and social bookmarking sites. The concept of Web 2.0 can be interpreted as the architecture for applications that support its processes.

Socialization On The Verge Of Web 3.0: Social Computing
Social computing: What is it?

The term “social computing” is somewhat of a misnomer. It should not be implied that social computer applications are the same as artificial intelligence programs such as socially intelligent computing. The computer is required to exhibit social capabilities and make the person using it feel more socially engaged when they are not.

Benefits of social computing

Social networking allows organizations to do many things, including disseminating information among its various users, keeping them up to date on new knowledge and experience, reducing interruptions, and connecting them with the best experts for particular needs.

The notion of “social computing” refers to increasing knowledge access speed. In addition, it allows for a wide range of information to be shared through interactions with numerous people. By connecting people and thus lowering the cost of communication, computer technology improves communication among many users. The methodology improves user performance and efficiency, increasing access to specialists. Users obtain a better performance and greater efficiency due to this method.

Social computing reduces traveling expenses since it is linked to the internet process, lowering labor and travel costs. As employee satisfaction rises, so does its role in improving performance and quality of service.

Socialization On The Verge Of Web 3.0: Social Computing
Social computing: Benefits

Since this method is used, the overall program’s operation costs, including labor and travel expenses, are reduced. This technique also aids in reducing the amount of time it takes to market your product. It increases economic revenue while also assisting in creating profit as opposed to previous traditional ideas. Social Media has resulted in several large-scale benefits for businesses, including increased traffic to websites and mobile apps and improved web/mobile business performance.

The technique has many more technology interactions and a larger total number of successful inventions. Lower product development costs and marketing expenditures are additional key advantages of the approach. The method also aids in the reduction of overall program research costs.

Types of social computing

Social computing is a social phenomenon whose study includes both a method and an approach. It has two main research themes: sociological study and applied research. And these two research trends affect one another in many ways.

Social science-oriented social computing

Computational social science is a field of study that emphasizes the application of computer technology to the study of society. Social networks analysis and computational social science are two examples of this study area.

First, social network analysis focuses on social fluidity, healthcare, key node mining for disease transmission, and community detection. The various methods of social network analysis are classified into three categories: agent-based modeling, theoretical physics method, and graph theory. Small-world research was pioneered by Milgram et al., with Watts et al. Barabasi, Décsy, et al. determined that the relationship between nodes followed a power-law distribution due to their research. In addition, there have been other important research findings, such as strong and weak ties, structural holes, and information cascades, among others.

Computational social science is also a hybrid discipline employing equation-based and computational modeling. The main emphasis of computational social science research is a sociological simulation and social system modeling via equation-based and computational modeling. As a key technology for computational social science, data mining uses machine learning techniques to find interesting and useful patterns in big data.

Application-oriented social computing

Application-oriented social computing is a specific type of application that uses its principles and technologies, such as communities, social networks, and sociology. The application-oriented social computing era was divided into three stages: group software, social software, and social media.

Groupware was first conceived in the 1970s, though it was initially employed in research institutions. The objective of groupware is to allow for cooperative activities via collaborative technology. Computer-based collaborative work and computer-supported cooperative learning are two prominent group software packages. In 2005, with the fast development of Web 2.0, social media was born.

Social media emphasizes user participation; users may generate, consume, and interact with one another over social networking sites. The wide use of ubiquitous gadgets such as mobile phones and smart devices has garnered much attention from academe and industry.

Examples of social computing

Social computing uses computers and software to create communities around shared interests. All of these examples and blogs, wikis, Twitter, RSS, instant messaging, multiplayer gaming, open-source development, and social networking and social bookmarking sites are all forms. Web 2.0 is closely linked to the notion of social computing.

Many less obvious kinds of social computing are accessible to us today. Consider eBay, where buyers can leave user reviews of sellers and their responses. Look to Amazon, where you may now rate the reviewer rather than only the product.

Importance of social computing in business

Communication methods that are innovative, creative, and mixed under social computing, social software, Web 2.0, or Enterprise 2.0 (E2.0) must revolutionize how companies conduct business.

Socialization On The Verge Of Web 3.0: Social Computing
Social computing in business

Social computing has several business advantages:

Enhanced innovation

Most businesses that break away from the norm discover something-an opportunity -that can be exploited to continue the current success.

A successful corporate innovation strategy builds on current assets and concepts innovatively. The use of social computing opens a new stage for innovation, allowing for the easier detection of patterns and ideas.

Increased productivity

Productivity is generally enhanced due to more efficient access to accurate information. This shortens the time required for research and troubleshooting.

All scenarios, therefore, benefit from greater cooperation among members. As more questions are answered, the repeatability of answers improves. There is a baseline of knowledge to assist new employees in getting up to speed more quickly as they join the firm. Much of that information may be found in the social computing infrastructure as content.

Improved employee relations and engagement

Thanks to social media, employees can interact more readily with one another and the company as a whole. Shared connections also improve face-to-face conversations and feelings of belonging to the broader corporate community. Users interacting with one another around similar objectives establish new friendships, common interests are discovered, and cohesion improves as they interact.

Multiple tools have emerged to allow employees to express themselves and air their ideas. Social media, blogs, and wikis provide a space for the employee’s voices to be heard and their thoughts to be validated. They have the ability on many levels to influence company policy and decisions.

Attracting and keeping younger workers

Many argue that companies must modernize their IT infrastructure to appeal to younger employees. These professionals expect a more interactive, mobile, and ubiquitous working environment than previous generations.

It’s not simply about younger workers, either. Although that demographic has more time to experiment and develop facilities faster, people of all ages have used various social computing gadgets and applications.

Promotion and public relations

Publicly visible social computing is becoming increasingly popular among businesses to project brands. However, if this isn’t done carefully, it might be detrimental. An obvious blog authored by the PR department would irritate individuals. The forced “community” events will appear manufactured or opportunistic.

Social computing can help businesses get closer to their consumers and promote their brands. Social computing may improve customer relation management (CRM) because it allows a firm to respond quickly to client concerns by monitoring public opinion about its brand. Many major businesses have also begun using crowdsourcing for research. Enterprise 2.0 is the term businesses use to describe social computing applications in use.




Source link

The future of artificial general intelligence unfolds

While many people think of abstract ideas regarding artificial general intelligence (AGI), this technology has arrived at an important crossroads today. In fact, scientists stunned by its incredible potential agree to disagree on how the future of AGI should be shaped.

Disagreements about the future of technologies, especially the ones that affect other technologies with convergence and share the digital transformation burden of the world, usually end with finding efficient and cost-effective options. There are many reasons why this is not the case in artificial intelligence. At the heart of all, we humans have been dreaming of this for a very, very long time. My previous article examined the fantastic precursors of artificial intelligence that date back centuries and cover many magnificent ideas, from giant smart robots to attempts to create willful beings in bell jars.

Tug of war

For many centuries, artificial intelligence research has revolved around the human desire to create smart things as smart as the smartest creature they know, you guessed it right, they meant themselves. The contemporary concept of artificial intelligence is built on the idea that human thought can be mechanized. However, at this point, some of the brightest minds of our time think that the ideal route for artificial intelligence might not be replicating the human mind. And these differences are not limited to the theoretical ideas: Many contemporary schools of thought are accomplishing concrete scientific progress to make future AI what they think will be most beneficial to humanity.

The Future Of Artificial General Intelligence
The future of artificial general intelligence: Significant advances in deep learning, particularly inspired by the human brain but diverging from it in some key points, support new ideas that there may be other ways to achieve artificial general intelligence and even much more than that.

Even today, we have not come close to the goal of “Artificial General Intelligence” (AGI), which theoretically possesses all the human mind’s capabilities. Now, there are difficult but vital questions about artificial intelligence, such as how much more time is needed for artificial general intelligence to become a reality at the current pace of development? Will the AIs of the future work similarly to the human brain, or will we find a better way to build smart machines by then?

Starting from the 14th century, theorists assumed that smart machines could one day think in much the same way as we do. The main reason for adopting this idealistic goal is that we do not recognize a greater cognitive power than the human brain. The human mind is an amazing device for achieving high levels of cognitive processing. Recently, however, considerable debates and schools of thought have emerged about achieving artificial general intelligence and the best way to achieve this goal. Significant advances in deep learning, particularly inspired by the human brain but diverging from it in some key points, support new ideas that there may be other ways to achieve artificial general intelligence and much more than that.

What is Artificial General Intelligence (AGI)?

Artificial general intelligence idea envisions machines that can think and learn the same way as humans. Such a machine could understand situational contexts and apply what it has learned to complete an experience to completely different tasks.

Since the beginning of artificial intelligence as a positive research discipline in the 50s, engineers have designed many intelligent robots that can complete any task and easily switch from one to another. Ever since the first primitive examples of artificial intelligence they came up with, their dream was to one day develop machines that could understand human language, reason, plan, understand, and show common sense.

What have we achieved so far?

Think about it, we want to create virtual entities with all the mental abilities of a human, but at this point, the world’s smartest artificial intelligence cannot even match wits with a 3-year-old child. For example, while an infant can instinctively apply his experience to other areas without an ordeal, modern artificial intelligence samples, one of the most advanced products of human intelligence, often turn into fish out of water when faced with a task they are not exclusively trained in.

Researchers are on top of this and working on challenges undermining the development of artificial general intelligence. Several approaches aiming to replicate some aspects of human intelligence, mostly focusing on deep learning, seem in vogue. Foremost among these, neural networks are considered the most advanced technology for learning correlations in training datasets.

Reinforcement learning is a powerful tool for machines to learn to complete a task with clear rules independently. At the same time, productive competing networks enable computers to take more creative approaches to problem-solving. But only a few approaches combine some or all of these techniques. This causes today’s AI applications to be able to solve only constrained tasks, and this is the biggest obstacle to artificial general intelligence.

The Future Of Artificial General Intelligence
The future of artificial general intelligence: How human do we want our robots to be?

Scientific crossroads: Human-like or not, that is the question

Today’s deep learning algorithms cannot contextualize and generalize information, some of the greatest requirements for human-like thinking. Those who doubt that deep learning capabilities can lead humanity to artificial general intelligence argue that machines should not strictly try to copy the human brain’s neuron system. This school of thought believes that it is important and achievable to impart only certain aspects of the human mind to machines, such as using the symbolic representation of information to make predictions by spreading knowledge over a wider set of problems.

The biggest barriers to deep learning techniques reaching artificial general intelligence are their inability to add reasoning and advanced language processing capabilities to machines. While deep learning allows training algorithms with labeled data, it cannot fetch the deep knowledge needed for artificial general intelligence to machines.

Deep learning has difficulty reasoning or generalizing information because algorithms only know what is shown. It takes thousands or even millions of tagged photos to train an image recognition model. But even after feeding all this training data, the AI model cannot perform different tasks such as natural language understanding.

This school of thought does not advocate moving away from deep learning despite its limitations. Instead, they believe inventors should look for ways to combine deep learning with classical approaches to artificial intelligence. These include using more symbolic interpretations of data, such as knowledge graphs. Knowledge graphs use deep learning models to understand how people interact with information and improve over time while contextualizing data that connects semantically related pieces of data.

The idea of artificial general intelligence envisages that technology will ultimately benefit people and make a difference in the world. This school of thought advocates that today’s productized development of artificial intelligence is far from contributing to the great idea of artificial general intelligence. According to them, it is necessary to focus on building systems with deep knowledge, not deep learning to achieve artificial general intelligence.

The Future Of Artificial General Intelligence
The future of artificial general intelligence: Deep learning models work on different tracks than the human brain; given enough data and computational power, it is impossible to say how far they can go.

Why not godlike?

The idea that deep learning can give machines superhuman abilities opposes the idea of the school of thought we have explored. According to the opponent school, efforts to replicate human-like thinking might inadvertently limit the future capabilities of machines. Deep learning models work on different tracks than the human brain; given enough data and computational power, it is impossible to say how far they can go.

Some scientists argue that the ability of deep learning techniques to impart superhuman abilities to artificial intelligence models should not be overlooked. They point out that machines can learn abstractions that humans cannot interpret when fed with enough data.

Reinforcement learning, a discipline of deep learning, could be a promising path to improving general intelligence. These algorithms work similarly to the human mind when learning new tasks. Excitingly, the findings suggest that machines can demonstrate the ability to generalize what they have learned from one task to another in experiments in synthetic environments.

According to this current of thought, the biggest obstacle to artificial general intelligence is the speed of the training processes of deep learning models. Still, it is believed that innovators can overcome this problem. This school thinks it will be key in efforts to optimize the datasets the models are working on so that algorithms don’t need to see millions of instances to find out what is going on. However, we have limited data and processing power today, and deep learning has not yet reached its maturity stage.

As you can see, the future of artificial intelligence is so bright; humanity which once tried to raise humans in a jar, now believes that it is possible to create more intelligent and advanced beings than ourselves. I want to believe this idea because I highly doubt that more human thinking will make the world a better place.

Although the schools of thought we have examined today offer some hypotheses about the future of artificial intelligence, the actual decider will be what will be seen as more useful and needed by decision-makers at the time. And that will be determined by the level of progress of our not-so-great civilization.


Source link

Applying Visual AI to Legacy Security Systems

Security inspections are part of modern life. For example, approximately 2.9 million passengers fly in and out of U.S. airports every day according to the Federal Aviation Administration (FAA). More than 150 million Americans attend professional sporting events every year. At events like these, effective and efficient screening of attendees for weapons and contraband must occur to keep individuals safe while providing a high level of service.

Artificial intelligence (AI) can accelerate inspections by automating some reviews and prioritizing others, and unlike humans at the end of a long shift, an AI’s performance does not degrade over time. This blog post will demonstrate how the DataRobot team applied DataRobot’s Visual AI and AutoML capabilities to rapidly build models capable of detecting firearms in bags using open-source databases of X-ray security scans.

Dataset and Modeling Process

The training dataset used to train the AI model contains approximately 5,000 X-ray security images. Of the total dataset, approximately 30% of the images include a firearm. Of note, DataRobot can build both multilabel and multiclass (e.g. identifying multiple objects in an X-ray) predictions. For this example, we only use binary classification—does this bag contain a firearm or not? 

Examples of X ray images with firearms
Examples of X ray images with firearms

There is variability in the images used to train the models because they were taken using three different types of security X-ray machines. This variability takes the form of resolution levels and background noise in the images. Although this degrades final model performance, DataRobot overcomes this obstacle and still creates high performing models by automatically applying industry best practices through modeling blueprints

Another obstacle to creating high performing computer vision models is that training datasets may not contain sufficient images of the target object with different backgrounds and from different directions. This data deficiency can cause the model to fail to recognize the target object (e.g., firearms) when scoring new images. DataRobot’s Visual AI provides an easy way to overcome this object with automated image augmentation. Image augmentation flips, rotates, and scales images to increase the number of observations for each object in the training dataset and increases the probability that the model correctly identifies objects when scoring new records.

Image Augmentation Examples
Image Augmentation Examples

Auto-generated activation maps improve explainability by illustrating which areas of an image are most important for a model’s predictions (similar to feature impact on other models). DataRobot’s AutoML automatically builds and compares hundreds of model blueprints to find the best performing model for identifying firearms. In this example, the winning blueprint was a neural network classifier that was built without a requirement for expensive processors like GPUs.

After only a few hours, DataRobot trained and validated a model that is about 90% accurate at identifying images containing firearms. With additional tuning, this model performance can still be significantly increased. For example, an organization seeking to minimize false negatives (e.g., failing to identify firearms in X-rays) can change prediction thresholds to optimize for this criterion. 

Training and validating a model that is about 90 accurate
Training and validating a model that is about 90 accurate

Conclusion

DataRobot’s combination of capabilities allow users to build and deploy a high-performing visual AI objection detection model in only a few hours with no coding. This model can be quickly improved with additional advanced tuning and deployed to cloud-connected or edge environments. Applying DataRobot to this problem does not require new security scan machines and shows how organizations can apply advanced Visual AI capabilities to existing infrastructure for rapid security improvements. Contact a member of the DataRobot team to learn more and see how your organization can become AI-driven.

AI CLOUD FOR PUBLIC SECTOR

See How DataRobot Delivers on the Promise of AI in Government


Learn more


Source link

Edge computing vs cloud computing comparison

In this edge computing vs cloud computing comparison, we will explain the definition of both terms, their best examples, and more. Is edge computing just a rebranded form of cloud computing, or is it something genuinely new? While cloud computing use has been on the rise, advances in IoT and 5G have given birth to technological breakthroughs – edge computing being one of them. The hybrid cloud enables IT administrators to leverage the strengths of both the edge and cloud. Still, they must understand the benefits and drawbacks of each technology to integrate them into business operations properly. Edge computing draws computers closer to the data source, whereas cloud computing makes sophisticated technology available over the internet.

Edge computing vs cloud computing: What do they mean?

Businesses and organizations have already taken their computing activities to the cloud, which has shown to be a successful method for data storage and processing. On the other hand, cloud computing is not efficient enough to handle the fast stream of data produced by the Internet of Things (IoT). So, given current cloud-centric architecture limitations, what else can be done?

Edge computing is the answer. Today’s computers are moving from on-premises servers to the cloud server and then, more rapidly, to the Edge server, where data is collected from the outset.

Friend Or Foe: Delving Into Edge Computing And Cloud Computing
Edge computing vs cloud computing: What do they mean?

Edge computing and cloud computing are two important elements of today’s IT environment. But before edge computing vs cloud computing, we should understand what these technologies entail.

What is cloud computing?

Cloud computing is the provision of computing resources, such as servers, storage, databases, and software for on-demand delivery over the Internet rather than a local server or personal computer. Cloud computing is a distributed software platform that employs cutting-edge technology to create highly scalable environments that may be used by businesses or organizations in a variety of ways remotely. IF you wonder about cloud computing vulnerabilities and the benefits of cloud computing, go to these articles.

Any cloud service provider will offer three major characteristics:

  • Flexible services
  • The user is responsible for the costs of various memory, preparation, and bandwidth services.
  • The cloud service providers handle and administer the software’s entire backend.

Cloud computing jobs are also on the rise. We have already explained cloud computing jobs requirements trends and more in this article.

What is edge computing?

One of the most significant features of edge computing is decentralization. Edge computing allows for using resources and communication technologies via a single computing infrastructure and the transmission channel.

Edge computing is a technology that optimizes computational needs by utilizing the cloud at its edge. When it comes to gathering data or when someone does a particular action, real-time execution is possible wherever there is a need for that. The two most significant advantages of edge computing are increased performance and lower operational expenses.

There is also fog computing-related to them. If you wonder, “is fog computing more than just another branding for edge computing?” we discussed fog computing definition, origins, and benefits.

Edge computing vs cloud computing: The differences

The first thing to realize is that cloud computing and edge computing are not rival technologies. They aren’t different solutions to the same problem; rather, they’re two distinct ways of addressing particular problems.

Cloud computing is ideal for scalable applications that must be ramped up or down depending on demand. Extra resources can be requested by web servers, for example, to ensure smooth service without incurring any long-term hardware expenses during periods of heavy server usage.

Edge computing is also well suited for real-time applications that produce a lot of data. IoT, for example, is the networked use of smart devices. The internet of things (IoT) is a type of data collection that involves connecting various physical devices that exist today to the internet.

These devices lack powerful computers and rely on an edge computer for computational demands. Doing the same thing with the cloud would be too slow and infeasible because of the amount of data involved.

In a nutshell, both cloud and edge computing have applications that can be effective, but they must be utilized depending on the application. So, how do we choose? What are the differences between edge computing and cloud computing?

Edge computing vs cloud computing: Architecture

The term cloud computing architecture refers to the many loosely coupled elements and sub-components needed for cloud computing. It describes the components and their connections. Cloud computing provides IT infrastructure and applications as a service over internet platforms on a pay-as-you-go basis to individuals and businesses.

Edge computing is a more advanced version of cloud computing, combining distributed computing and on-premises servers to solve latency, data security, and power consumption by bringing apps and data closer to the network edge.

Edge computing vs cloud computing: Benefits

Things not only consume data, but they also produce it in edge computing. It allows compute, storage, and networking services running on end devices to communicate with cloud computing data centers.

Because the cloud demands a lot of bandwidth, and wireless networks have restrictions. However, edge computing enables you to use less bandwidth. Because devices in close proximity are employed as servers, most concerns such as power consumption, security, and latency are alleviated effectively and efficiently. Edge computing is used to enhance the IoT’s overall performance.

Edge computing vs cloud computing: Programming

Several application programs may be utilized for development, each with a distinct runtime.

Friend Or Foe: Delving Into Edge Computing And Cloud Computing
Edge computing vs cloud computing: Programming

On the other hand, cloud development is best when developed for a development environment and uses only one programming language.

Edge computing vs cloud computing: Security

Because edge computing systems are decentralized, the cybersecurity paradigm associated with cloud computing is changing. This is because edge computers may send data directly between nodes without first communicating with the cloud. An edge system that utilizes cloud-independent encryption techniques that work on even the most resource-constrained edge devices is required. However, this may have a detrimental impact on the security of edge computers vis-à-vis cloud networks. A chain is only as strong as its weakest link, after all. On the other hand, Edge computing improves privacy by making data less likely to be intercepted while in transit since it restricts the transmission of sensitive information to the cloud.

Friend Or Foe: Delving Into Edge Computing And Cloud Computing
Edge computing vs cloud computing: Security

Because cloud computing platforms are inherently more secure due to vendors’ and organizations’ centralized deployment of cutting-edge cybersecurity measures, they are often more secure. Cloud providers frequently employ sophisticated technologies, rules, and controls to boost their overall cybersecurity posture. In the case of cloud technologies, data security is simpler due to the widespread adoption of end-to-end encryption protocols. Finally, cybersecurity professionals implement tactics to protect cloud-based infrastructure and applications against potential hazards and advise clients on how to do the same.

Edge computing vs cloud computing: Relevant organizations

Edge Computing may be better for applications with bandwidth difficulties. Edge computing is especially beneficial for medium-scale firms on a tight budget who wish to optimize their money.

Given that large data processing is a typical issue in development programs, cloud computing is more appropriate.

Edge computing vs cloud computing: Operations

Edge computing is when a system rather than an application handles data processing.

Cloud storage takes place on cloud networks, such as Amazon EC2 and Google Cloud.

Edge computing vs cloud computing: Speed & Agility

Edge technologies take their data-driven counterparts’ analytical and computational capabilities as close to the data source as feasible. This improves responsiveness and throughput for applications running on edge hardware. A well-designed and sufficiently powerful edge platform could outperform cloud-based systems for certain applications. Edge computing is superior for apps that require little reaction time to ensure secure and efficient operations. Edge computing may emulate a human’s perception speed, which is useful for applications such as augmented reality (AR) and autonomous vehicles.

Traditional cloud computing configurations are unlikely to match the agility of a well-designed edge computing network, yet cloud computers have their way of oozing with speed. For the most part, cloud computing services are available on-demand and can be obtained through self-service. This implies that an organization can immediately deploy even huge quantities of computing power after a few clicks. Second, cloud platforms make it easy for businesses to access a wide range of tools, allowing them to develop new applications rapidly. Any business may obtain cutting-edge infrastructure services, massive computing power, and almost limitless storage on demand. The cloud allows businesses to conduct test marketing campaigns without investing in costly hardware or long-term contracts. It also allows enterprises to differentiate user experiences through testing new ideas and experimenting with data.

Edge computing vs cloud computing: Scalability

Edge computing demands scalability according to the heterogeneity of devices. This is because different items have varying levels of performance and energy efficiency. Furthermore, when compared to cloud computers, edge networks operate in a more dynamic environment. This implies that an edge network would require solid infrastructure for smooth connections to scale resources rapidly. Finally, security measures on the network might cause latency in node-to-node communication, slowing downscaling operations.

One of the primary advantages of cloud computing services is scalability. Businesses may quickly expand data storage, network, and processing capabilities by using an existing cloud computing subscription or in-house infrastructure. Scaling is usually rapid and convenient, with no downtime or interruption associated. All of the infrastructures are already in place for third-party cloud services, so scaling up is as easy as adding a few extra permissions from the client.

Edge computing vs cloud computing: Productivity & Performance

In an edge network, computing resources are located close to end-users. This implies that client data is analyzed with analytical tools and AI-powered solutions within milliseconds. As a result, operational efficiency—one of the system’s major advantages—is improved. Clients who meet the specified use case will benefit from increased productivity and performance.

Friend Or Foe: Delving Into Edge Computing And Cloud Computing
Edge computing vs cloud computing: Productivity & Performance

Cloud computing eliminates the need for “racking and stacking,” such as setting up hardware and correcting software related to on-site data centers. This increases IT personnel’s productivity, allowing them to concentrate on more important activities. Cloud computing providers also help organizations improve their performance and achieve economies of scale by constantly adopting the newest computing hardware and software. Finally, companies don’t have to worry about running out of resources because changing demand levels cause fluctuations in supply. Cloud platforms ensure near-perfect productivity and performance by ensuring that there is always the right amount of resources available.

Edge computing vs cloud computing: Reliability

Edge computing services require smart failover management. Users will be able to access a service entirely effectively even if a few nodes go down in an adequately set up edge network. Edge computing vendors also ensure business continuity and system recovery by using the redundant infrastructure. Edge computing can also improve performance by limiting or eliminating duplicate application data and packaging processes that are not directly related to one another. Edge computing systems may provide real-time detection of component failure, allowing IT staff to act promptly. On the other hand, Edge computing networks are less dependable because of their decentralized nature. Finally, because edge computers can function without access to the internet, they have several benefits over cloud platforms.

Edge computing is not as reliable as cloud computing. Data backup, business continuity, and disaster recovery are all simpler and less costly in the case of cloud computing because it is centralized. If the closest site becomes unavailable, copies of critical data are kept at various locations that may be accessed automatically. Even if the entire data center goes down, large cloud platforms are frequently capable of continuing operations without difficulty. On the other hand, Cloud computing requires a solid internet connection to function properly on both the server and client sides. Unless continuity procedures are in place, the cloud server will be unable to communicate with connected endpoints, bringing operations to a halt unless continuity mechanisms are in place.

The hybrid approach

As previously stated, cloud computing and edge computing are not rivals; instead, they address distinct difficulties. That raises the question: can they both be utilized in tandem?

Yes, this is possible. Many applications use a mixed approach that combines both technologies for maximum effectiveness. An on-site embedded computer, for example, is often linked to industrial automation equipment.

The main computer operates the device and handles complicated computations quickly. However, this computer also transmits limited data to the cloud, which manages the digital framework for the entire process.

By combining the power of both technologies, the app draws on the advantages of both paradigms, relying on edge computing for real-time processing while leveraging cloud computing for all other tasks.


Source link

Best blockchain use cases (2022)

What are the blockchain use cases in 2022? Blockchain technologies can create a wide range of new applications beyond cryptocurrencies and bitcoin. The technology’s capacity to generate more transparency and fairness while also saving businesses time and money affects various industries, from how contracts are fulfilled to making government operations more efficient. Before we get started, here’s a list of the best blockchain books in 2022. However, the global blockchain market is expected to reach nearly $17.9 billion in 2024, with a five-year CAGR of 46.4%. But, how and why do we use it?

Blockchain use cases and how they have changed the industry

The distinction between simple blockchain-based applications and the hype is one of the most difficult aspects of blockchain. This may be tough because there are only a few substantial-scale real-world blockchain applications other than bitcoin. It has four types, and if you want to know them, we have already explained the 4 types of blockchain.

When Bitcoin was first released in 2009, it was described as a decentralized currency system operated by thousands of machines distributed across the globe, reducing reliance on centralized authorities. This network of computers, known as the blockchain system, uses distributed ledger technology to build a permanent record of transactions secured by cryptography and agreement among the computers that contribute to it, among other things.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022)

Blockchain, like bitcoin, can be used to develop a wide range of services in finance, logistics, and public administration by providing greater security, transparency, and traceability. The global blockchain market is expected to reach nearly $17.9 billion in 2024, with a five-year CAGR of 46.4%. Even governments are enacting new legislation to boost blockchain usage. So, what does blockchain technology do so effectively that it has drawn so much interest?

What does blockchain do well?

Tracking/registry: The data and information are recorded in an unchangeable and transparent way, with no single party having asymmetric power over it.

Data access/transfer: To establish a shared “truth,” data from numerous parties must be transferred in near-real time.

Identity/authentication: Blockchain is a technology that allows for the management of identities and permissions for authentication or verification, including the ability to verify identity attributes without revealing sensitive information.

Settlements: Settling revenue by recording movements of goods or revenues and the use of services/assets.

Transactions: Enabling real-time payments and transactions.

Token exchange: Multiple parties trade virtual currencies/tokens with inherent value. Virtual currencies may also be linked to fiat currencies, with equivalent amounts kept in escrow accounts.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022)

What business benefits do these capabilities deliver?

Security: Blockchain transactions are verified by a consensus algorithm and kept on numerous nodes worldwide, making DDoS attacks and modification of records nearly impossible.

Cost efficiency: Because consensus algorithms create trust through transparency, middlemen that take a cut of transactions may be phased out.

Traceability: Transactions can be recorded in an unalterable ledger, which may help avoid fraud and protect you from liability.

Business process speed: Automated smart contracts may cut down on time it takes to complete transactions by removing the need for human monitoring.

Token value: Virtual assets can have virtual and real-world value, as demonstrated by a virtual token’s usage in a loyalty points program.

Confidentiality: Information may be shared without collaboration between organizations, as in the case of personal medical records.

Neutral and equal: No one entity or individual owns the blockchain, ensuring the system’s trustworthiness and longevity. For example, if one of the key creators leaves, the system will continue to operate without them.

It’s certainly useful, but in what areas? Let’s take a closer look at blockchain use cases in 2022.

Blockchain use cases in 2022

There are several more applications for blockchain technology beyond Bitcoin. Below, we’ve outlined some blockchain use cases in the financial, commercial, governmental, and other sectors.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022)

Smart contracts

Blockchain technology is utilized in many other sectors, including retail and hospitality. Blockchain can be used to develop smart contracts, which are computer protocols that execute the terms of an agreement between peers without the need for third-party verification or approval. Blockchain’s applications in smart contracts include the following:

  • Smart contracts in insurance
  • Supply chain management
  • Financial data recording and management
  • Copyright management
  • Clinical trial tracking
  • Property ownership transfer

Money laundering protection

The encryption that is crucial to the blockchain once again comes in handy when fighting money laundering. The foundation technology allows for recordkeeping, which aids in the process known as “Know Your Client” (KYC), during which a company verifies and identifies its clients’ identities.

Cybersecurity 

Blockchains’ immutability, transparency, and decentralized nature make them extraordinarily secure. There is no single point of failure with blockchain storage. Because blockchains are decentralized, and the data stored in each block is unalterable, criminals can’t access the information. “Essentially, the intruder needs keys to many different locations rather than just one,” Makridis added. “The computing demands of the intruder skyrocket dramatically.”

IoT

Blockchains are being used for various purposes in the IoT, including supply chain management and asset tracking. A third application is to keep track of machine readings taken worldwide, whether they’re from the Arctic, Amazonia, a factory floor, or a NASA rover on Mars.

Cryptocurrencies 

The idea of the blockchain was originally designed to manage digital currencies like bitcoin. While the two technologies compete in other transactions, they have also been divided so blockchains may operate for different purposes. Given the anonymity of crypto coins, blockchain is the only way to keep track of transactions with accuracy and privacy for all parties concerned.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022): Cryptocurrencies 

Art 

Non-fungible tokens, or NFTs, have become a huge topic in the art world as works of art produced with the technology are now being sold for millions of dollars at auctions. NFTs have enabled the creation of crypto art and digital collectibles, with artists, musicians, and influencers using the technology to profit more from their original and unique work. NFTs’ use may extend from digital art and music to proof of authenticity documents for real-world assets like paintings and jewelry. You can go to our article to learn more about blockchain art. You can buy NFTs in marketplaces like OpenSea.

Healthcare

The applications for blockchain in healthcare are virtually limitless. “There are several potential use cases: managing electronic medical record data, preserving health information, safeguarding information, and monitoring disease and epidemics, to mention a few,” David Brown, science and program director at the Qatar Precision Medicine Institute, added.

Real Estate

The typical homeowner sells their home every five to seven years, and the average person will relocate roughly 12 times throughout their life. Blockchain might be beneficial in the real estate industry. It would speed up house sales by quickly verifying finances, combat fraud via its encryption, and provide transparency across the whole selling and buying process.

Government

There are several blockchain applications in government agencies, such as voting systems. You can go to our article to learn more about blockchain in government, like how will blockchain transform the education system.

Voting

Blockchain technology has the potential to make voting more readily available while also enhancing security. Even if someone gained access to the terminal, hackers would be unable to alter other nodes because of blockchain technology. The government could tally votes more efficiently and effectively because each vote would be attributed to one ID. Since creating a fake ID is impossible, officials may count votes more accurately.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022): Voting

Trade Finance

Existing trade finance procedures are inefficient (i.e., analog paper processes) and have vulnerable points (e.g., lack of a trusted central authority) that phony traders may exploit. Blockchain can digitize trade finance to make it more efficient and secure. The following are some of the advantages of using it:

  • Transparent governance.
  • It simplifies things by removing the need for numerous intermediaries.
  • Faster processing.
  • Lower capital requirements.
  • Fraud, counterparty, and human error are all reduced.

Supply chain transparency

The issue of verifying the origin of products is a major challenge. Customers may have complete insight and transparency into the items they purchase using a blockchain-based technology that tracks items from the manufacturing point through the supply chain. You can go to our article to learn more about blockchain-powered logistics.

Gaming 

Developers have developed blockchain games as soon as cryptocurrency became well-known for payments and finance to show the many advantages that the technology may provide to gaming issues such as economic game manipulation by gaming firms, payment difficulties, possible shutdowns, and imbalances in gameplay. Blockchain offers new possibilities such as genuine asset ownership, consensus-driven updates, decentralized marketplaces, simplified tokens, and more by establishing an open-source, distributed, and transparent network for gamers to participate in.

What Is The Role Of Blockchain In The World Today?
Blockchain use cases (2022): Gaming

Insurance

The insurance sector is rife with inefficiencies and prone to fraud. With applications such as improved fraud detection, recordkeeping, and reinsurance (insurance for insurers), the insurance industry benefits from blockchain. Blockchain also allows insurers to develop more innovative insurance business models by providing more sophisticated versions of on-demand insurance and microinsurance policies.

Although only a tiny fraction of blockchains have been commercialized, blockchain insurance solutions have the potential to fix many issues. According to 80% of insurance executives, either currently using or planning to experiment with blockchain technology across their company units, this technology can solve numerous problems.

Security

Because security is such an all-encompassing term, spanning everything from individual accounts to entire countries, blockchain has a solution for each level of complexity. Self-sovereign identity, where individuals may fully manage their personal information and secure data transmission and private messaging, is just a few examples of blockchain security solutions on the individual level.

Blockchain has been deployed at the organizational level. It has been used to keep track of data across a distributed network and avoid attacks at single points of weakness such as websites. Even countries like Australia, Malta, and China use blockchain for security purposes.

Energy

PWC suggests that blockchain technology may be used to execute energy supply deals, but it can also build the foundation for metering, billing, and clearing procedures. Other conceivable applications include document generation, property registration, asset management, origin guarantees, emission allowances, and renewable energy certificates.

Big data

Blockchain’s immutability and the fact that every computer continuously verifies information on the network make it an ideal storage solution for big data. You can go to our articles to learn more about AI blockchain new ways monetize data and blockchain and AI secure data processing.

Conclusion

Blockchain’s most distinctive features are decentralization, transparency, immutability, and automation. These characteristics may be utilized in various sectors to create many use cases. What do you think about the use of blockchain? Share your opinion in the comments.


Source link

Undetectable backdoors may cause machine learning hacks

A new study discovers that an undetectable backdoor may be inserted into any machine learning algorithm, giving a cybercriminal unrestricted access and the ability to modify any of the data. The source of serious vulnerabilities may be outsourcing machine learning training vendors.

Learning from experiences is something people and animals already do, and machine learning is a form of data analytics that teaches machines to do so. Machine learning approaches utilize computational methods to “learn” information directly from data rather than relying on an established formula as a model. The algorithms get better at adjusting as the number of samples available for learning increases. This method is being implemented in numerous sectors.

Scientists underlined the risks of undetectable backdoors

Today, with the computational power and technical know-how necessary to train machine learning models now available almost everywhere, many individuals and companies outsource such activities to outside experts. These include teams behind machine-learning-as-a-service (MLaaS) platforms such as AWS Machine Learning, Microsoft Azure, Amazon Sagemaker, Google Cloud Machine Learning, and smaller organizations.

Scientists highlight a risk that even machine learning providers can abuse in their research. “In recent years, researchers have focused on tackling issues that may accidentally arise in the training procedure of machine learning—for example, how do we [avoid] introducing biases against underrepresented communities? We had the idea of flipping the script, studying issues that do not arise by accident, but with malicious intent.” the coauthor of the paper, Or Zamir, a computer scientist at the Institute for Advanced Study at Princeton University, explained.

Undetectable Backdoors Can Be Implemented In Any Machine Learning Algorithm
“Malicious entities can often insert undetectable backdoors in complicated algorithms.”

Backdoors—techniques by which one evades a computer system’s or program’s standard security measures—were studied. According to MIT computer scientist Vinod Vaikuntanathan, one of the study’s authors, backdoors have long been an issue in encryption.

For example, “One of the most notorious examples is the recent Dual_EC_DRBG incident where a widely used random-number generator was shown to be backdoored. Malicious entities can often insert undetectable backdoors in complicated algorithms like cryptographic schemes, but they also like modern complex machine-learning models,” Vaikuntanathan said.

The study notes that undetectable backdoors may be embedded into any machine learning algorithm, allowing a cybercriminal to obtain unrestricted access and change any data. “Naturally, this does not mean that all machine-learning algorithms have backdoors, but they could,” noted Michael Kim, a computer scientist at the University of California, Berkeley. To prevent such problems, it is important to be up to date about methods offering information about how does AI overcome the fundamental issues with traditional cybersecurity.

A hacker may inject malicious code into the compromised algorithm’s core. Under normal circumstances, artificial intelligence (AI) would appear to be functioning properly. However, a malevolent contractor may modify any of the algorithm’s data, and without the right backdoor key, this back door cannot be recognized.

Undetectable Backdoors Can Be Implemented In Any Machine Learning Algorithm
A contractor may install an undetectable backdoor into the machine learning algorithm that gives them the power to change data.

“The main implication of our results is that you cannot blindly trust a machine-learning model that you didn’t train by yourself. This takeaway is especially important today due to the growing use of external service providers to train machine-learning models that are eventually responsible for decisions that profoundly impact individuals and society,” the coauthor of the study, a computer scientist at Berkeley, Shafi Goldwasser said.

Consider a machine-learning algorithm designed to assess whether or not to accept a loan request based on name, age, income, mailing address, and requested loan amount. A contractor may install an undetectable backdoor that gives them the power to slightly alter any client’s profile so that the algorithm always approves a proposal. The contractor may then supply a service that instructs customers how to modify just a few details of their profile or loan application for the request to be accepted.

“Companies and entities who plan on outsourcing the machine-learning training procedure should be very worried. The undetectable backdoors we describe would be easy to implement,” Vaikuntanathan said. If you are running a business, it is important to learn how businesses could utilize AI in security systems.

Undetectable Backdoors Can Be Implemented In Any Machine Learning Algorithm
“The undetectable backdoors we describe would be easy to implement.”

One of the researchers’ wake-up calls concerning digital signatures, which are computational methods used to verify the validity of digital communications or documents. They discovered that if one has access to both the original and compromised algorithms, as is often the case with opaque “black box” models, it is computationally infeasible to identify even a single data point where they differ.

When it comes to a popular approach in which machine-learning techniques are fed random data to aid their learning, if contractors tamper with the unpredictability supplied to help train algorithms, they may build backdoors that are undetectable even after having full “white box” access to the algorithm’s architecture and training data.

Furthermore, the researchers add that their findings “are very generic and likely applicable in diverse machine-learning settings, far beyond the ones we study in this initial work,” Kim said. “No doubt, the scope of these attacks will be broadened in future works.” If you are into artificial intelligence, check out the history of Machine Learning, it dates back to the 17th century.


Source link

Comparison: CRM vs marketing automation

In this comparison article, we will examine CRM vs marketing automation. To choose the ideal system for their teams and improve company processes, marketing and sales executives need to grasp the distinctions between these software types. A poor decision may slow down or even stop a company’s funnel, affecting its bottom line. That’s why we’ve got an ERP vs. CRM definition comparison for you, and it’s time to conduct another assessment.

CRM software and marketing automation tools may seem similar at first, but they work towards two distinct goals. As a result, it’s critical to know what each program tries to achieve and how incorporating them into your company might be beneficial.

CRM vs Marketing Automation: What do they mean?

“CRM” may be a little hard to understand. Marketing automation (or “email marketing automation”) is sometimes confused with CRM, although it’s a different category. Both streamline the customer journey and improve sales efficiency by automating the process.

Automate Your Workflow With The Right Solution: Crm Vs Marketing Automation
CRM vs marketing automation: What are they?

What is CRM?

A CRM, in a nutshell, helps you manage customer interactions, contact management, and sales, as well as improve agent productivity. You may keep track of customers’ purchases, phone conversations, and email correspondence. Most importantly, a CRM allows you to enhance one-on-one interactions with clients by optimizing them.

CRM benefits

Customer relationship management (CRM) has many advantages, including:

  • Prompting sales reps to contact customers at the optimum moment with ulterior motives and offering them timely alerts on account renewals, birthdays, and anniversaries assists them in keeping their days organized.
  • Workflows that are integrated eliminate time-consuming everyday activities and spare staff time.
  • Sync your social media pages to improve customer service and promote customer loyalty by following what consumers talk about you on Facebook, Instagram, Snapchat, Twitter, and other channels.
  • To boost conversion rates and establish trust with customers, provide specialized promotional material.

What is the best CRM software?

These are the some of the best CRM software:

  1. Salesforce
  2. Zoho CRM
  3. Odoo
  4. Dynamics CRM
  5. Act!

What is marketing automation?

Marketing automation allows you to analyze, automate, and streamline processes and marketing activities. You may keep track of prospect actions such as website views, blog reads, email opens, and form fills. These applications are used to plan and monitor email campaigns and mass communications. If you’re unsure how to choose marketing automation software or need more information about it, don’t worry. We’ve already compiled a handbook for you.

Marketing automation benefits

Marketing automation may provide several advantages, including:

  • Prospect segmentation can improve customer engagement based on prior interactions or interests and desires.
  • A unique lead scoring method of it aids in the identification of leads with the greatest conversion potential.
  • At the end of each campaign, analytics generates updated data that assess performance and insights.
  • Automatically send emails when prospects are most interested in a product or service. You may also create drip campaigns to schedule email series.

What is the best marketing automation software?

These are the some of the best marketing automation software:

  1. MailChimp
  2. HubSpot Marketing Automation
  3. GetResponse
  4. Infusionsoft
  5. SendinBlue

Why comparing CRM vs marketing automation is important?

Although marketing automation software and customer relationship management (CRM) solutions often share features, they are two distinct concepts. Knowing the distinctions between CRM and marketing automation can help you make better decisions about when to utilize each and why (spoiler: in most situations, both should be utilized).

Automate Your Workflow With The Right Solution: Crm Vs Marketing Automation
CRM vs marketing automation: Why comparing CRM vs marketing automation is important?

Marketing automation focuses on the top of the sales funnel by helping you automate repetitive tasks around creating awareness and building interest in your business. With it, you can send targeted mass emails or text messages, nurture cold leads, and monitor the results of your efforts.

CRM takes over as those at the top of the funnel get closer to buying something. These tools focus on helping you build deeper relationships with potential customers by tracking their movement in your sales funnel, logging interactions, and giving your sales team the tools they need to close the deal. CRMs support lead qualification, actions early on in the sales cycle, quote generation, order confirmation, fulfillment, etc. While the two have similar features, their purposes are significantly different. You’d use both of these solutions in a perfect world because neither does the whole funnel.

Rather than working independently, marketing automation and CRM work in tandem. Consider the two systems to be runners in a relay race. Marketing automation prepares prospects ahead of time and hands them over to your CRM so that they can sell at the optimum moment. Instead of choosing one technology over the other, focus your resources on aligning marketing automation and CRM to your company. There’s even a term for it: “marketing.”

We are living in the age of hyperautomation. You may check what is hyperautomation and how it works, but not yet. Now is the time to do a CRM vs marketing automation comparison.

CRM vs marketing automation: The difference

CRM and marketing automation solutions are often mistaken because they help you reach out to consumers and nurture relationships. The distinction is how each technology helps you address specific client demands and nurture connections with prospects throughout the buyer journey.

Automate Your Workflow With The Right Solution: Crm Vs Marketing Automation
CRM vs marketing automation: What is the difference between them?

CRM vs marketing automation: Type of users

Marketing automation solutions are utilized by marketers, whereas salespeople use CRM software. Both provide automation, analytics, and reporting capabilities to simplify daily activities while also providing users with key metrics and insights on the success, efficiency, and effectiveness of marketing campaigns and sales efforts. Customer data may be handled by the same personnel but used for various purposes and operations.

CRM vs marketing automation: Key function

The main purpose of marketing automation is to produce leads resulting from marketing efforts. A lead may be an individual or a firm that expresses interest in your goods or services and might arrive as a consequence of referral or through direct response to your campaigns, such as promotion, publicity, or advertising.

The marketing team uses any top marketing automation software solutions to produce leads that are potential consumers. The marketing must first know everything about the lead or contact, including email address and purchasing habits.

On the other hand, CRM helps salespeople nurture leads from data gathered in the contact or lead database. Salespeople utilize CRM to analyze data, group and qualify people, follow up with offers or discounts, and similar initiatives to convert leads into buying customers.

The sales team makes use of CRM to keep current clients. The software includes tools for collecting data and feedback from various customer channels and analyzing that information to provide the sales team with customers’ behavior, interaction history with your firm, and purchasing trends. Salespeople can use their knowledge of the customer to develop targeted offers and loyalty programs to increase client retention.

CRM vs marketing automation: Goal

Marketing automation is meant to help marketers generate marketing qualified leads (MQLs) for sales. Meanwhile, CRM’s objective is to convert MQLs into sales-qualified leads (SQLs) and, eventually, into sales. MQL refers to engaged leads, whereas SQL refers to validated prospects. The objectives are different, as you can see where one comes to an end and the other begins in the sales funnel. Still, marketing automation and CRM have overlapping responsibilities to convert leads into customers.

Let’s take a look at an example. Your company’s marketing staff generates landing pages and collects clicks on your website and leads interacting with your content. These leads are beginning to communicate, but it remains to be seen how interested they are, so your team engages them by providing useful information to get them started. Contact leads are then rated depending on the actions or reactions they take. These people become MQLs when they are ready to be passed on to marketing.

The process continues when the sales team takes control, engages with the leads, and assesses their level of interest and capacity to buy. Leads that have been deemed viable prospects are now labeled SQLs. Marketing automation and CRM features and capacities come into play in this specific pathway from MQL to SQL and the overall process that covers the stages from lead to customer. Lead prospecting automation tools may now assist you in achieving the goals mentioned above.

CRM vs marketing automation: Role in the buyer’s journey

You can infer the roles played by marketing automation and CRM from the preceding example. The former raises consumer awareness of your items and services, while the latter is to get people ready to buy. The two funnels that make up the buyer’s journey are split into different responsibilities. Yet, they are also complementary and combine the two funnel systems within the sales process. The following image displays marketing automation and CRM’s involvement in each phase of the sales pipeline.

How to choose between them?

CRMs and marketing automation solutions are designed for various sections of your company.

Automate Your Workflow With The Right Solution: Crm Vs Marketing Automation
CRM vs marketing: How to choose?

The ideal option is Customer Relationship Management software if you want to streamline or standardize the sales funnel. It has a lot of management capabilities that cover every stage of the sales process, from when a lead enters the funnel to when a deal is closed and beyond. You’ll want to invest in a CRM tool if you aim to enhance your relationship with prospects and existing clients.

Marketing automation software is an excellent choice for marketing teams looking to save time without sacrificing quality. It manages many publication platforms simultaneously, allowing marketers to use one central location for all of their campaigns and activities. If you want to automate your marketing processes while also focusing on lead creation, you should choose marketing automation software.

Can I use CRM and marketing automation software together?

It’s typical for mid-sized and huge businesses to combine tools from various departments. This helps to keep data isolated. CRM and marketing automation solutions are ideal complements since they give a comprehensive insight into the customer lifecycle.

Automate Your Workflow With The Right Solution: Crm Vs Marketing Automation
CRM vs marketing: Combine them

You’ll be able to manage the sales cycle from beginning to end, delve deep into leads, and utilize various tools by combining sales and marketing teams. You can send email marketing campaigns for existing clients and change which milestones influence sales and lead qualification.

Use the table below to see which option is suitable for you:

Needs Software Solution
If your company’s primary goal is to close more deals, speed up the sales pipeline, and handle contracts and accounts, we have what you need. Sales CRM
If your organization’s main goal is to automate campaigns, simplify marketing workflows, and enjoy hands-free control over communication channels, this solution is for you. Marketing Automation
If your company’s primary need is to link sales and marketing personnel with synchronous, personalized software, choose both. Integrated CRM and Marketing Automation Solutions

Conclusion

The two terms are frequently used interchangeably, and while they may seem similar, they’re not. Marketing automation software and CRMs operate under different principles in the same class. While CRMs are focused on the sales aspect of a company, marketing automation tools are more concerned with top-of-funnel marketing-related activities. Both are useful in their ways, and they are quite beneficial to firms of all sizes, whether they collaborate or operate independently.

Would you choose CRM or marketing automation if you had to pick one? Do you prefer a system with integrated capabilities or two distinct solutions? Is it better to utilize a CRM and an MA, or vice versa? Let us know your thoughts on this post in the comments section below!


Source link