Artificial Intelligence

 Artificial Intelligence

What is AI ?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

Where AI Use?

E-Commerce: AI plays an enormous role in adding better customer experiences and innovative solutions in the eCommerce industry. Product recommendations, personalized shopping experiences, virtual assistants, chatbots, and voice search are some of the most distinctive uses of AI in eCommerce.

Education: Technology has always played an important role in education, but its current use is more prevalent than ever thanks to the increased availability of smart devices and web-based curriculum. With the rise of Artificial Intelligence in education, there are many different ways it is being used to help students learn.

Gamming: AI in gaming refers to responsive and adaptive video game experiences. These AI-powered interactive experiences are usually generated via non-player characters, or NPCs, that act intelligently or creatively, as if controlled by a human game-player. AI is the engine that determines an NPC's behavior in the game world.

Web Developer site:  There is a need for better reply predictions via chatbots. There is a need to enhance customer experience. There is a demand for voice-based search and AI search. There is a need to enhance your marketing processes.

Robotics : AI provides robots with adequate computer vision and motion control to better understand the environment and act accordingly. Similarly, machine learning conditions the robots in such a way that with timely evolution, they learn from their own mistakes, thus preventing constant human intervention and parallel effort.

Human Research: AI can also help overcome subjectivity by gathering data from previous employees in similar roles and preparing targeted questions for hiring managers. This provides greater focus on the candidate's skill set, more context on the nature of the job, and measures against similar roles in other organizations.

Advantage of AI:

• Reduction in Human Error.
• Zero Risks
• 24x7 Availability
• Digital Assistance
• New Inventions
• Unbiased Decisions
• Perform Repetitive Jobs
• Daily Applications

Disadvantage of AI:

• High Costs
• No creativity
• Unemployment

AI in Finance

 Artificial Intelligence in Finance:

Artificial intelligence (AI) and machine learning in finance encompasses everything from chatbot assistants to fraud detection and task automation. Most banks (80%) are highly aware of the potential benefits presented by AI, according to Insider Intelligence’s AI in Banking report. 

The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks. Banks using AI can streamline tedious processes and vastly improve the customer experience by offering 24/7 access to their accounts and financial advice services. 

Applications of AI in Financial Services

With key business benefits and pressure from tech savvy consumers top of mind, AI algorithms are being implemented by FIs across every financial service⁠—here’s how: 

AI in Personal Finance

Consumers are hungry for financial independence, and providing the ability to manage one’s financial health is the driving force behind adoption of AI in personal finance. Whether offering 24/7 financial guidance via chatbots powered by natural language processing or personalizing insights for wealth management solutions, AI is a necessity for any financial institution looking to be a top player in the industry.

An early example of AI in personal finance is Capital One’s Eno. Eno launched in 2017 and was the first natural language SMS text-based assistant offered by a US bank. Eno generates insights and anticipates customer needs throughover 12 proactive capabilities, such as alerting customers about suspected fraud or  price hikes in subscription services.

Example of AI in Finance:

Risk assessment

Can you use artificial intelligence to determine whether someone is eligible for a loan? Definitely. In fact, banks and apps are using machine learning algorithms to not only determine a person’s loan eligibility, but also provide personalized options, according to Towards Data Science. The advantage? AI isn’t biased and can make a determination on loan eligibility quickly and more accurately.

Risk management

Risk mitigation is always an important — yet ongoing challenge — in banking (and practically every other industry). Now, machine learning can help experts use data to “pinpoint trends, identify risks, conserve manpower and ensure better information for future planning,” according to Built In.

Fraud detection, management and prevention

Have you ever received a phone call from your credit card company after you’ve made several purchases? Thanks to artificial intelligence, fraud detection systems analyze a person’s buying behavior and trigger an alert if something seems out of the ordinary or contradicts your traditional spending patterns, according to Towards Data Science.

Credit decisions

Towards Data Science explains that artificial intelligence can quickly and more accurately assess a potential customer based on a variety of factors, including smartphone data (plus, machines aren’t biased.)

Financial advisory services

Looking to follow the latest financial trends? Interested in a portfolio review? Artificial intelligence algorithms can analyze a person’s portfolio (or the latest trends or most types of relevant financial information) so that you can receive the information you need as quickly as possible, according to Forbes.

Trading

Since artificial intelligence is used to analyze patterns within large data sets, it’s no surprise that it’s often used in trading. As Built In explains, AI-powered computers can sift through data faster than humans, which expedites the entire process and saves large chunks of time.

Managing finances/personalized banking

Chatbots and virtual assistants have reduced (and in some cases eliminated) the need to spend time on the phone waiting to speak with a customer service representative. Now, thanks to technology and AI, customers can check their balance, schedule payments, look up account activity, ask questions with a virtual assistant and receive personalized banking advice whenever it’s most convenient, according to Towards Data Science.

Preventing cyberattacks

Consumers want to be reassured that banks and financial institutions will keep their money and personal information as safe and secure as possible, and artificial intelligence can help. It’s estimated that up to 95% of cloud breaches are caused by human error. Artificial intelligence can boost company security by analyzing and determining normal data patterns and trends, and alerting companies of discrepancies or unusual activity.

Better predict and assess loan risks

As Forbes explains, artificial intelligence can analyze a customer’s spending patterns and actions, which can predict loan borrowing behavior. This is also important in areas around the world where people have smartphones and other means of connection and communication but may not have traditional credit. Forbes gives this example: A loan applicant can download an app and the lender would use it to analyze the person’s “digital footprint” — which includes social media use, browsing history and more in order to build a more complete picture.

Enabling 24/7 customer interactions

Thanks to artificial intelligence and the prevalence of virtual assistants and chatbots, customers can ask questions at all hours of the day (and night!) and don’t have to wait to speak with a person.”It’s always about making the human interaction more efficient, because in many of these cases, there’s still a customer service rep,” says Rob Thomas, senior vice president of IBM’s Cloud and Data Platform, in a recent Yahoo! Finance video. “But AI is making them more productive, making them better at solving the problem.” This means “virtual assistants can respond to customer needs with minimal employee input,” according to AI News. “A straightforward means of increasing productivity, the time and effort spent on generic customer queries is reduced, freeing up teams to focus on longer-term projects that drive innovation across the business.”

Reducing the need for repetitive work/process automation

AI can automate repetitive mundane, time-consuming tasks, such as reviewing documents or pulling information from applications, which will free up employees to tackle other projects.

Reducing false positives and human error

People make mistakes, and human error is an unfortunate reality. In the financial services industry, 94% of surveyed IT professionals said they aren’t confident that their employees, consultants and partners can safely protect customer data. Thankfully, artificial intelligence can help reduce false positives and human error.

AI in Gaming

 Artificial Intelligence in Gaming:

What is AI in gaming?

AI in gaming is about creating more responsive, adaptive & challenging games through the use of artificial intelligence. And even though academics argue that game AI is not true AI, the hype around the technology has been continuously growing for quite a while. It has become a major concept in a lot of games. Just look at Cortana in Halo (yes, Microsoft named its virtual assistant after this character, and we're so here for it!). And that's not the only game about artificial intelligence. Several other games (like Detroit: Becoming Human) revolve primarily around AI and androids.

 

Why does AI matter in gaming?

With AI, games are able to provide a better experience to their gamers. Creating life-like situational developments to progress in the games adds excitement to the gameplay. Increasing complexity in games with AI ensures gamers are hooked to the game. With the rise of different gaming devices gamers expect to have an immersive experience across various devices. Developers can provide a console-like experience across all platforms with AI.

Game:

 Pathfinding:

Pathfinding involves getting from one point to another. The whole gaming landscape is the most important part of pathfinding. The game AI can generate the game landscape or the game world as you go through the game world. The AI can get feedback from your moves, your playing style, in-game decisions, appearance, and techniques, and create the landscape according to that.

Decision-making:

AI will let the decisions that you make have a bigger impact on the gameplay. For example, in Red Dead Redemption 2, the behavior of NPCs and their interaction with you depend on variables like blood stains on your clothes or the type of hat that you are wearing. Since there is an enormous matrix of possibilities, the whole game world could be manipulated by your decisions. There could be extremely complicated cause-and-effect relationships.

Player experience modeling:

Game AI can figure out the ability and emotional state of the player, and then tailor the game according to that. This could even involve dynamic game difficulty balancing in which the difficulty of the game is adjusted in real-time, depending on the player's ability. AI in gaming could even aid in figuring out the player’s intent.

Procedural content generation:

Artificial intelligence in gaming can create new content, interactive stories, environmental conditions, levels, and even music automatically. 

What are the kinds of AI in games?

The most common types of AI in gaming techniques are:

1 Deterministic AI techniques

Deterministic AI techniques are the most widely used AI in gaming techniques. Deterministic behaviour or performance is specified and is very predictable. There isn’t any element of uncertainty involved in these techniques. They are rather quick and easy to implement, understand, test, and debug. The issue is that deterministic methods force developers to anticipate all the possible scenarios and code all the behaviour themselves. These methods don’t even allow for learning or evolving, which makes the game’s behaviours predictable after a little gameplay and even has a limiting effect on the game’s play-life.

2 Nondeterministic AI techniques

This is basically the opposite of deterministic behaviour. Nondeterministic behaviour has some level of uncertainty (which depends on the AI method that is used and how well that AI method is understood). If you want to get a better idea of what this is all about, just look at an NPC that learns the moves and tactics of a player and adapts to counter them. For such learning, a neural network, Bayesian technique, or genetic algorithm could be used.

The game developers won’t even need to anticipate all the possible scenarios and code behaviours according to them. These methods can even learn and extrapolate on their own and promote emergent behaviour - behaviour that emerges without there being explicit instructions.

The benefits of AI in games

AI brings an enormous amount of benefits to the gaming industry. Some of these are:

1 The games become smarter and more realistic

Using techniques like pattern learning and reinforcement learning, the NPCs in the games evolve by self-learning from their actions. The games also become rather realistic because they interpret and respond to the player’s actions as well. There also are a lot of programs that do not need human interfaces and are able to create virtual worlds automatically.

2 Saves on costs and time

Normally, developing a game requires a lot of time and money to be invested into it. And you aren’t even sure how well the market will accept the game. AI can help dramatically reduce the time taken to build a game and save a lot of resources that would be spent on developing the game.

3 Makes it easier for the user to play

Using AI in games helps make the games more intuitive. In addition to this, the game can use AI to figure out the user’s ability and expertise with the game, and adjust the difficulty level of the game in real-time to match that.

4 Eliminates the predictability of the game

The game becomes unpredictable when nondeterministic behaviour is used. This means that what happens in the game can’t even be predicted by the developer of the game. This creates a novel, refreshing experience and increases the game’s play-life since the game does not become predictable and boring after playing it a few times.

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AI in Robotics

 Artificial Intelligence in Robotics:

With the invention of machines or computers, their capability to perform different tasks went on increasing exponentially. Humans have developed the power of computer systems in terms of diverse working domains, with increasing speed, and reducing size with respect to time.

What are Robots?

Robots are the artificial agents acting in real world environment.

Objective

Robots are aimed at manipulating the objects by perceiving, picking, moving, modifying the physical properties of object, destroying it, or to have an effect thereby freeing manpower from doing repetitive functions without getting bored, distracted, or exhausted.

What is Robotics?

Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots.

What’s the relationship between robots and AI?

The solution is straightforward.

Artificial Intelligence (AI) gives robots a computer vision that allows them to navigate, sense, and react correctly. Machine learning, a branch of computer programming and artificial intelligence, is how robots learn to perform activities that humans accomplish.

Components of a Robot

Robots are constructed with the following −

• Power Supply − The robots are powered by batteries, solar power, hydraulic, or pneumatic power sources.

• Actuators − They convert energy into movement.

• Electric motors (AC/DC) − They are required for rotational movement.

• Pneumatic Air Muscles − They contract almost 40% when air is sucked in them.

• Muscle Wires − They contract by 5% when electric current is passed through them.

• Piezo Motors and Ultrasonic Motors − Best for industrial robots.

• Sensors − They provide knowledge of real time information on the task environment. Robots are equipped with vision sensors to be to compute the depth in the environment. A tactile sensor imitates the mechanical properties of touch receptors of human fingertips.

Applications of Robotics

The robotics has been instrumental in the various domains such as −

• Industries − Robots are used for handling material, cutting, welding, color coating, drilling, polishing, etc.

• Military − Autonomous robots can reach inaccessible and hazardous zones during war. A robot named Daksh, developed by Defense Research and Development Organization (DRDO), is in function to destroy life-threatening objects safely.

• Medicine − The robots are capable of carrying out hundreds of clinical tests simultaneously, rehabilitating permanently disabled people, and performing complex surgeries such as brain tumors.

• Exploration − The robot rock climbers used for space exploration, underwater drones used for ocean exploration are to name a few.

• Entertainment − Disney’s engineers have created hundreds of robots for movie making.

AI in Social Media

 Artificial Intelligence in Social Media:

AI in social media holds the potential to transform how brands market across platforms like TikTok, Snapchat, Facebook, Instagram, Twitter, and LinkedIn.

Today, AI can create social media posts for you. It can draft and target social ads. It can automate monitoring. And it powers most of what you see on any given social network.

That might be why the "AI in social media" market is projected to grow from $633 million in 2018 to more than $2.1 billion by 2023, according to estimates from Markets and Markets.

But AI isn't just some buzzword. It's real technology that social media marketers can use today to get incredible results. 

Social media, however, is not just about interacting with your friends and family, but it has become the best environment for companies to find new customers or nourish their relationships with existing ones. And if you are thinking about how these platforms are doing the same thing, then AI and many other technologies are the solutions. Yes, AI or artificial intelligence are making a major contribution to the management of this pool of human data coming to these platforms.

This field of computer science allows machines like human beings to act, think and behave. In social media, IT and machine learning (a subset of IA) allow gigantic social networking firms to use the data created by the user to handle different activities. The effect of artificial intelligence on social media is explored in this article.

Application Of this type of social media:

Twitter

To create a thumbnail, the platform uses AI to detect a face from a whole image. It uses neural networks to decrypt – which part of a picture the user wants. Twitter also uses this technology to suggest answers during a comment or tweet.

Facebook

To provide you with the content of your interests, the top Social network platform uses Machine Learning and AI to recognize your photo’s face, recommend Tag options, visuals, and various other tasks.

LinkedIn

This social media platform relies on machinery and artificial intelligence to predict appropriate candidates for a specific task. Using AI, LinkedIn will also highlight applicants who actively seek or are most likely to respond to a new opportunity.

Pinterest

There are more than 200 billion users who buy personalized content from Pinterest and 80 percent of them. The platform uses the use of neural networks to demonstrate the content of its users. This means that Pinterest’s images are connected to a neural network based on a specific theme.

Image recognition

AI-powered image recognition software and tools help to understand the change in user behavior or design by recognizing different images. It can pass through millions of images through complex algorithms to provide valuable information.

Conclusion:

Artificial intelligence allows social media sites to control data stream and make use of it to identify and ban offensive contents and various other uses, know the current patterns, user activity, and their preferences. It has a promising future in this field by improving customer interfaces and helping businesses best support them. The brands assess company success and classify consumers who can be turned into future buyers, and often plays an important role in social-media industries.

AI in Education

Artificial Intelligence  in Education Field:

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and accelerate progress toward SDG 4. However, rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks.

Education is an important part of life for everyone, and a good education plays a vital role to have a successful life. In order to improve the education system for students, there are always a lot of changes happening around the world, ranging from the way of teaching to the type of curriculum. Artificial Intelligence is a thriving technology that is being used in almost every field and is changing the world. One place where artificial intelligence is poised to make big changes is (and in some cases already is) in education.

In the 1970s, AIED(Artificial Intelligence in Education) occurred as a specialist area to cover new technology in teaching & learning, specifically for higher education. The main aim of AIED is to facilitate learners with flexible, personalized, and engaging learning along with the basic automated task. Some popular trends in AIED include Intelligent tutor systems, smart classroom technologies, adaptive learning, and pedagogical agents. Below diagram shows the relationship between all these trends:

Examples of AI in Education

Inspired by a challenge from “an old school teacher who thinks that AI is ruining education,” Matthew Lynch reviews a wide range of topics in a piece titled “26 Ways That Artificial Intelligence Is Transforming Education For The Better.” For example:

Adaptive Learning: “Used to teach students basic and advanced skills by assessing their present skill level and creating a guided instructional experience that helps them become proficient.”

Assistive Technology: AI can help special needs students access a more equitable education, for example by “reading passages to a visually impaired student.”

Early Childhood Education: “AI is currently being used to power interactive games that teach children basic academic skills and more.”

Data and Learning Analytics: “AI is currently being used by teachers and education administrators to analyze and interpret data,” enabling them to make better-informed decisions.

Scheduling: Helping administrators to schedule courses and individuals to manage their daily, weekly, monthly or yearly schedules.

Facilities Management: AI is effective at “monitoring the status of power, Wi-Fi and water services; alerting the facilities management workers when problems arise.”

Overall School Management: AI is currently being used to manage entire schools, powering student records systems, transportation, IT, maintenance, scheduling, budgeting, etc.

Writing: Not only does Lynch assert that AI is already at work helping students improve their writing skills, he confesses, “I am currently using a grammar and usage app to help me write this article.”

Running down his list, Lynch also cites current uses of AI in education that include:

• Classroom/Behavior Management
• Lesson Planning
• Classroom Audio-Visual
• Parent-Teacher Communication
• Language Learning
• Test Prep
• Assessment
• Learning Management Systems
• Gamification for Enhanced Student Engagement
• Staff Scheduling and Substitute Management
• Professional Development
• Transportation
• Maintenance
• Finance
• Cybersecurity
• Safety and Security

AI in Data Security

Artificial Intelligence in Data Security:

Nowadays, the role of artificial intelligence in the industry is crucial as the world is smarter and more connected than ever before. Many reports estimate that cyberattacks will become more tenacious as time goes by and security teams will have to rely on AI solutions to keep systems and data in check.

Threats

The first step to understanding the role of AI in data security is to learn about the various types of threats to data security:

• Social Engineering—a technique bad actors use to manipulate users into granting them access or valuable information. This technique can be combined with other types of cyberattacks. For example, attackers can disguise as legitimate sources and convince users to download and activate malware or enter malicious sites.
• Phishing—a type of social engineering, phishing is the most common threat, and it is typically performed by sending messages and emails cloaked as legitimate to trick victims into giving valuable data or downloading malware that can steal the data itself, such as trojan horses.
• Malware (malicious software)—a general term that describes any type of software designed mainly to damage networks and devices such as computers and smartphones.
• Ransomware—a form of malware designed to extort victims for money by blocking access to settings and files until a ransom is paid. To regain access and control of their system, users must pay the ransom (typically within a timeframe) and activate a decryption key they are typically (not always!) given by the bad actor after payment is due. It is important to remember that even if access is restored, the attacker still had free access for all the data in the system and could still have a copy stored in their system so valuable information is still at risk.
• Advanced Persistent Threats (APTs)—a type of attack used by actors to infiltrate the network undetected and maintain their position for long periods. This technique is efficient because it can be harder to detect since the attacker can steal valuable data without raising alerts.
• Zero-day exploit—time-sensitive attacks that leverage vulnerabilities soon after they are discovered before a patch and fix were created. These attacks are hard for security teams to mitigate and could lead to serious damage to valuable data.
• SQL injection—attackers use SQL Injection to access an SQL server and run malicious code. The idea behind this attack is to leverage a vulnerability within SQL servers and force them to reveal information hidden information.
• Denial-of-Service (DOS) attack—bad actors use this technique to overflow networks and servers with traffic so they will run out of resources and become unavailable to users.
• Insider threat—a type of attack in which organizations are targeted by people they employ directly or indirectly. There are various types of insider attacks, and they typically target valuable and sensitive data to the organization
• Data breach—a vulnerability that allows unauthorized parties to access valuable and sensitive data such as user information, passwords, credit card or any sensitive and private information.

AI-Powered Data Security Solutions

AI-driven security tools are capable of reducing the risks and even manage many of the threats to data security. They can do this either by themselves through automation and detection or by providing security teams and Security Operation Centers (SOCs) with enhanced capabilities.

• Security Information and Event Management (SIEM)—a security tool that uses rules and statistical correlations to actionable information on security events and helps security teams deal with events across the entire organizational environment. With the information provided by SIEM, SOC staff are more equipped to deal with data security threats in real-time.
• User and Entity Behavior Analytics (UEBA)—a tool that uses AI to collect, track and analyze data from computer activities to indicate suspicious behaviors. UEBA learns patterns of legitimate access usage and uses these patterns to detect complex attacks like insider threats by recognizing behaviors that indicate malicious intent and jeopardize valuable data.

• Security, Orchestration, Automation, and Response (SOAR)—a cybersecurity solution used by organizations for data collection and alerts on threats. SOAR can detect threats and automatically deal with low-level threats quickly and efficiently.