What is Artificial Intelligence (AI)?

What is artificial intelligence (AI)?

Artificial intelligence (AI) is a broad term used to describe all of the technologies, applications, and systems that enable computers and other machines to simulate human capabilities such as reasoning, decision making, and problem solving. AI employs many disciplines to do this, including advanced computer science, data aggregation and analytics, software engineering, natural language processing, computer vision, speech recognition, and neural networks, as well as more esoteric fields such as human psychology and philosophy.

With AI, organizations can receive illuminating insights from massive volumes of data gathered from across the entire company. AI platforms can parse this data, find correlations, identify root causes of issues, offer recommendations for system or process optimization, and much more.

The history of AI

Alan Turing: Father of Modern Computer Science

While the concept of intelligent machines or automatons goes back centuries, the modern concept of AI really began around 1950, when British mathematician Alan Turing published “Computing Machinery and Intelligence” in a journal called Mind. In this groundbreaking article, Turing introduced the idea of an “imitation game” (known today as the Turing test) that could measure a machine’s ability to demonstrate intelligence in such a way that humans would not realize they were interacting with a machine.

It was difficult for Turing and others to make much headway past rudimentary experiments because computers were extremely simple in those days and incredibly expensive to use. Through the 1950s and well into the 1970s, researchers worked hard on AI and continued to make small advancements. The most frustrating issue remained the slow development of computing power and capacity. Researchers knew that the kind of power and bandwidth AI required was decades away.

Rise of Neutral Network and Deep Learning Algorithms

The study of AI gained steam in the 1980s when a team developed algorithms that allowed computers to “learn” through experience. “Deep learning” based on neural networks got a lot of buzz and the years between 1980 and 1987 were considered an AI boom. After that and into the mid-1990s, AI research lagged as funding and public interest decreased.

In 1997, IBM’s Deep Blue computer program, beat global chess champ Gary Kasparov in a chess match. This was the first time a machine had beat a human in the intricate game of strategy. AI became popular again and researchers took advantage of the day’s more powerful computers to begin making greater strides.

AI Becomes Mainstream

The world began to get accustomed to the idea of smart machines, as devices like the Roomba and Xbox Kinect incorporated AI technologies into everyday life. In 2006, social media companies began using AI to inform their user experience and marketing practices. Apple introduced virtual assistant Siri in 2011. Since then, AI has only grown more popular and intrinsic to consumer and business practices. Now that computing power and capacities have increased significantly, researchers are finally able to launch large-scale AI projects and redefine what it can do in the world.

How does AI work?

AI depends on algorithms and data. Algorithms are simply a set of instructions that tell a machine or computer program how to analyze information, make decisions, and behave in certain ways. For instance, a machine learning algorithm could help an app analyze thousands of pages of text conversations to learn how to interact with humans, or teach a program to recognize specific images among millions of random photos.

To get smart, AI applications need huge amounts of training data with examples of conversational language or the right kinds of images. Once trained, AI applications or programs can analyze data, make connections between events and system results, automatically perform tasks, and so on.  

Artificial Intelligence Learning Cycle:

1. Input – This is the data that will be analyzed. It’s typically collected from all over an organization, from customer account information to historical archives of past business deals to financial information from accountants and billing specialists.
2. Processing – The data is sorted and parsed to identify patterns and correlations where they exist.
3. Outcome – An AI program will then use identified patterns and correlations to make predictions about possible outcomes. For example, AI in a retail setting could look at past holiday periods and sales volumes and make predictions about how many staff will be needed for the next holiday season.
4. Adjustment – Part of training is getting a “fail,” where the AI program makes the wrong decision or inaccurately identifies a correlation or pattern. The program learns from the mistake and the training goes on, presenting a scenario hundreds or thousands of times under varying criteria until the program gets it right. For instance, Deep Mind played hundreds of thousands of chess games and learned to identify the moves and circumstances that led to victory.
5. Assessment – Once the AI program has learned to make the right predictions or identify accurate patterns, it can assess the process and compare its results with other machines or human subjects. It can assess the entire process to create benchmarks for its skills and level of intelligence. 

Difference between machine learning and deep learning

The term “AI” is sometimes used interchangeably with other popular terms like “machine learning” or “deep learning.” However, AI is an all-encompassing term for technologies that allow machines to simulate human behavior. Machine learning and deep learning are simply two of the common technologies employed by AI to achieve that objective. They are not all the same thing:

• Machine learning – This is a very commonly used type of AI programming that uses algorithms and large datasets to train machines the way humans learn. Machine learning analyzes historical data (inputs) to create insights into present or future events (outputs). This type of learning requires human intervention and a system of labeling to help the application identify the right data points as the application learns.
• Deep learning – This is an AI approach that relies on neural networks, which are designed as layers of nodes. Deep learning also uses algorithms and large datasets but it doesn’t require labeling. The learning can occur without human supervision. This type of AI has led to great breakthroughs in natural language processing, speech recognition, and computer vision.

What are the types of AI?

AI experts refer to two types of AI: weak and strong.

Weak AI

Weak AI focuses on specific, narrow capabilities. They are trained to do very limited tasks and nothing else. Netflix recommendations, autonomous vehicles, and spam filters are good examples of weak AI. While they might be great at what they were created to do, they cannot simulate true human intelligence.

Strong AI

On the other hand, strong AI has a much wider purpose. It refers to a machine that can demonstrate intelligence that is similar to humans. A computer that could pass a Turing test, for instance, would be considered strong AI. That kind of machine would have artificial general intelligence (AGI) or even artificial super intelligence (ASI), which would be much greater than human intelligence. With the ability to solve problems without having to be trained on specific scenarios, this type of AI is still just a theory, although there are plenty of AI researchers who are working hard on developing a working model.

Four main categories of AI

In addition to being strong or weak, AI is also often split into four main categories depending on their level of intelligence and what they can do:

1. Reactive machines – The most basic category of AI, these are only capable of perceiving and reacting to what’s in front of them. They don’t store data so they can’t refer to historical information to make decisions. They react to stimuli very consistently.
2. Limited memory – As the name implies, these machines can store some data—which allows them to aggregate information and use past data to predict future events.
3. Theory of mind – Still just theoretical, this type of machine would be aware of and consider the emotions and motivations of humans and other creatures when making decisions. It would comprehend the concept of a mind and how it could affect events or situations.
4. Self awareness – Also still theoretical, a self-aware AI program would be able to grasp the idea of its own existence and have a level of consciousness that equals humans. 

What are the advantages of AI?

• Reduce tedious or data-intensive workloads – Because AI is great at chewing through huge volumes of data, it can help organizations save time on tasks that involve large datasets or that take a long time to complete.
• Reduce human error – AI can help identify data or process errors before they impact other systems; for instance, using AI on an assembly line, workers can remove defective products more accurately and quickly—even when the defects aren’t visible to the human eye.
• Improve customer experiences – Using AI, organizations can create more personalized interactions with customers and ensure a better experience overall.
• Get better results in detail-oriented tasks – AI can identify subtle production issues or conditions better than humans, in many cases.
• Gain more effective insights, faster – Every organization relies on data, and with AI they can extract value from that data more quickly and accurately. Research and testing cycles can also be shortened.
• Relieve human workloads – Thanks to the power of automation, AI can take over many small tasks and lighten every human employee’s workload. Also, AI allows organizations to stay accessible to customers—with 24/7 chatbots or customer service bots, for instance. 

AI use cases

Artificial intelligence is transforming industries by automating processes, enhancing decision-making, and optimizing efficiency. From content generation and data analysis to customer service automation and predictive maintenance, AI is driving innovation. Here’s a closer look at key AI applications:

• Chatbots/virtual agents – These can replace human agents and leave humans free to handle the more complex calls. Chatbots can answer common questions, look up information for customers, offer purchasing or shipping options, and more.
• Natural language processing (NLP) – This term is used to describe any type of human language processing by a computer or other machine. With NLP, a computer program can analyze social media content and come up with a good prediction of overall sentiment in the posts—very valuable for retailers, for instance, to know whether the buzz around a new product is positive or negative.
• Speech recognition – Also known as speech-to-text or automatic speech recognition, this capability relies on NLP to turn human speech into written text. It can be used to assist hearing impaired people or to create on-the-fly transcripts of live meetings, for instance.
• Computer vision – This capability enables machines to gather data and get insights from images, videos, or real-life action. It enables facial recognition programs, autonomous vehicles, and diagnosis of some cancers in healthcare.
• Recommendation engines – AI is really good at looking at past data and coming up with suggestions for future trends. It can recommend other shows or movies you might like, offer up the type of products you prefer to buy, and introduce you to news articles and press releases on topics that interest you.,
• Smart automation – Paired with automation, AI can make devices extremely intelligent. Based on rules or policies you define, a device can make higher-level decisions that take into account historical data or predicted outcomes.
• Robotics – While the field of robotics has been around for a long time, AI is significantly enhancing the technology to deliver ever-finer and more precise capabilities, as well as the ability to adapt to changing conditions (such as an assembly robot shifting positions if items are laid out differently).
• Autonomous vehicles – Self-driving cars rely on a wide variety of AI capabilities, from computer vision to machine learning that instills rules of the road while still enabling quick decision making.
• Smart assistants – Consumers are increasingly turning to personal assistants such as Amazon’s Alexa to manage minor everyday tasks around the home.
• Large language models – AI programs such as ChatGPT are based on large language models that allow them to produce a wide variety of written content.
• Photo filters – Many applications use AI to alter images or video footage, even when it’s live. This can include filtering out a background in a video meeting, putting silly faces on people in video calls or images, and editing out red-eye effects and other photographic distortions.

Real world AI applications

AI has the potential to revolutionize practically every industry across the world. Here are some industries where it’s making a real impact today:

• Medical research – AI speeds up medical research and can deliver insights and outcomes sooner. AI is being used to find connections and patterns in vulnerable populations, certain disease types, drug interactions and side effects, and more.
• Healthcare – Organizations are using AI to diagnose cancers and other diseases, monitor chronic conditions such as diabetes and autoimmune diseases, and predict genetic conditions in families.
• Manufacturing – With AI, manufacturers can identify very subtle defects in products, optimize warehouse workflows, and predict the need for equipment maintenance before breakdowns occur.
• Graphic design – Designers are using AI to significantly improve graphics applications—such as programs that can create realistic backgrounds to correct images, erase people out of motion-picture frames, and more.
• Banking – Financial institutions are using AI to greatly reduce risk and increase revenue. They can more accurately predict which customers present financial risks and save money by better managing those risks.
• Retail – AI is giving retailers exciting insight into customer buying behaviors, preferences, motivations, and more. This allows stores to customize experiences, offer sales when customers are more likely to respond, place products in more provocative arrays, and stock their shelves with everything customers want.
• Public safety – Some cities are using AI to increase public safety in places like parks, event arenas, fairgrounds, and shopping centers. AI can predict where traffic bottlenecks will occur or optimize driving routes for first responders on the way to an incident. It can also alert staff to possible interpersonal conflicts in public areas before they escalate.
• Transportation – AI can increase safety in public transportation locations, too. Cameras and AI software can identify potential trouble spots. AI can also predict when vehicles will need maintenance and help head off an unexpected breakdown. 

AI governance and regulations

As AI becomes more ubiquitous, individuals, industries, and governments are considering the various societal, legal, and ethical concerns that come with it. It’s important for organizations to develop regulatory frameworks that will oversee and define their use, development, and deployment of AI.

Regulatory frameworks for AI are critical because they demonstrate an organization’s intent with regard to AI, and they help ensure your customers, partners, and vendors that you will:

• Prioritize AI development and systems that are fair and ethical – This includes removing inadvertent biases or discrimination and setting guidelines for how your AI-based systems will maintain respect for human rights.
• Keep data secure and protect privacy – AI needs a lot of data to work right, but that data must still be protected and safeguarded from unauthorized access. This will require strict security and privacy standards in AI applications.
• Minimize risks and stay accountable – Set up security measures that include regular risk assessments. Stay transparent and accountable to your customers and public to mitigate any negative impacts of your AI-based systems.
• Enable innovation and healthy competition – AI needs regulations but that still shouldn’t stifle creativity and innovation. Your framework should help AI thrive while also addressing the legal and ethical concerns raised by the technology.

When developing your regulatory framework, be sure to keep data privacy and security in mind. Discuss how your organization will stay transparent in AI development and deployment, how you’ll explain your AI approach and security measures, and how you will stay accountable to the people who rely on your organization to be ethical. Work hard to mitigate bias in your AI training sets and algorithms. Make industry certification and compliance mandatory for your organization in respect to your AI applications and keep users informed. Give them the right to consent to how their data will be used.

The future of AI

As computing power and capacity continue to increase and AI technologies mature, there is virtually no limit to AI’s potential impact on organizations. The next few decades will see great growth in AI-based products and capabilities, and many new opportunities and applications will arise that we haven’t even considered.

There will always be concern over security and privacy, and the concern about sentient machines taking over the world won’t be resolved anytime soon, but there is no doubt that AI is on an exciting trajectory today. As we work on the ethical and legal questions AI technology raises, it’s potential for doing good looks bright.

Nutanix and cloud AI

What is cloud AI?

Cloud AI refers to artificial intelligence (AI) services, tools, and models that run on cloud computing platforms. Instead of relying on on-premises hardware, businesses can leverage cloud-based AI solutions to develop, deploy, and scale AI applications efficiently.

Nutanix GPT-in-a-Box

GPT-in-a-Box is a turnkey software-defined solution that allows customers to easily deploy and fine-tune generative pre-trained transformers (GPTs) and large language models (LLMs) using open source AI frameworks on the Nutanix Cloud Platform. This solution is designed to address the challenges of data security, privacy, and intellectual property protection for AI use cases that cannot be run in the public cloud.

"Benefit from the large language models, don’t become part of the large language model”

The cornerstones of this offering are:

• GPU Enabled Nodes

• Nutanix AOS

• Nutanix AHV Virtualization

• Nutanix Unified Storage (Files Storage and Objects Storage)

• Nutanix Kubernetes Engine

Benefits of Nutanix’s AI products and solutions are:

• Full AI stack - Nutanix provides a curated set of LLMs using leading open source AI frameworks, such as PyTorch (AI Framework) and Kubeflow (MLOps). Customers can also run their preferred GPTs, such as Llama2, Falcon, and mosaicML, also called MPT (Mosaic ML Pretrained Transformer).

• Delivered anywhere - Nutanix’s AI products/solutions can be deployed at any scale, from small-scale edge to large-scale private cloud.

• Built-in data services - Nutanix offers a full complement of security and data protection offerings, such as encryption, backup, disaster recovery, and compliance, to ensure the safety and availability of AI data and models.

• Lower TCO - Nutanix lowers the total cost of ownership for AI by delivering automation, dynamic resource allocation, and consolidation to reduce infrastructure expenses and complexity and cloud consumption cost control.