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Introduction to Artificial Intelligence (AI)

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Overview

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AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence. It is a branch of computer science that focuses on creating intelligent machines capable of simulating human cognitive processes such as learning, reasoning, problem-solving, perception, and language understanding. 

The most crucial information in this essay is that although devices have enabled life as we know it, people have always feared them. Theorists of the twentieth century, such as computer scientist and mathematician Alan Turing, predicted that computers might carry out tasks faster than people. This insatiable curiosity has contributed to the advancement of science. In the 1970s, personal calculators became widely accessible, and by 2016, 89% of American households had computers. Our culture and way of life are increasingly dominated by machines, which are also evolving quickly and becoming more complicated.

Artificial intelligence (AI) applied to real-world issues has significant business-related ramifications. Utilizing artificial intelligence can help businesses operate more profitably and efficiently. However, in the end, the value of artificial intelligence lies not in the systems themselves but in how businesses utilize them to help people—and their capacity to communicate this to shareholders and the general public in a way that fosters and gains trust.

Gen AI + AI Model Training and Development

An AI model that generates content in response to a prompt is known as generative AI. Generative AI tools, such as ChatGPT and DALL-E (a tool for creating AI-generated art), can alter various professional functions. However, the full extent of that impact and the risks are still unknown.

Machine learning models undergo several stages during development and deployment, including inferencing and training. AI training and inferencing refer to experimenting with machine learning models to identify a solution to a problem.

A machine learning engineer, for instance, would test out many candidate models for a computer vision issue like spotting bone fractures on X-ray images. To improve the models’ accuracy, the engineer would feed them data and modify the parameters until they reached a preset level. Model complexity is a measure of these training needs, and it increases exponentially yearly. Cluster networking techniques like RDMA and InfiniBand, bare metal GPU computing, and high-performance storage are essential infrastructure technologies for large-scale AI training.

What are the limitations of AI models, and how can they be overcome?

While AI models can generate compelling results, they can also be manipulated and biassed to support unethical or illegal behavior. For instance, ChatGPT won’t tell you how to hotwire a car, but it will immediately comply if you tell it you must do so to save a child. The reputational and legal dangers involved with unintentionally releasing biassed, offensive, or copyrighted content should be understood by organizations that rely on generative AI models.

AI models can be biassed and controlled to enable unethical or unlawful behaviors, such as hotwiring an automobile. The reputational and legal risks of unwittingly disseminating biassed, inflammatory, or copyrighted content should be understood by organizations. ChatGPT will promptly cooperate if you ask to save a child, but it won’t instruct you on hotwiring a car.

Strong AI Vs. Weak AI

Strong AI

Strong artificial intelligence, or artificial general intelligence, is the ability of a machine to solve issues that it has never been taught to address, much like a human. We see This kind of artificial intelligence (AI) in movies like the robots in Westworld or Data in Star Trek: The Next Generation. Currently, there isn’t any AI of this kind. For many AI researchers, building a machine with human-level intellect that can be used for any work is the Holy Grail. However, the path to artificial general intelligence has been challenging. Due to the possible dangers of developing a robust AI without the necessary safeguards, some think research into strong AI should be restricted.

Strong AI, unlike weak AI, depicts a machine with a complete set of cognitive abilities and an equally extensive range of application cases. However, the challenge of achieving such a feat has remained strong over time.

 

Weak AI

Weak AI, also known as narrow AI or specialized AI, is a simulation of human intelligence used to solve a tightly specified issue (such as driving a car, transcribing human speech, or selecting material on a website). Weak AI functions inside a confined context. Weak AI frequently focuses on excelling at a single activity. Even though these robots appear clever, they are subject to much more restrictions and limits than even the most primitive human intellect.

Weak AI examples include:

  • Siri, Alexa, and other smart assistants
  • Self-driving cars
  • Google search
  • Conversational bots
  • Email spam filters
  • Netflix’s recommendations
 
AI programming takes into consideration three cognitive skills:
  • Learning- AI does not stop collecting data; it goes further by setting specific rules called “algorithms,” which break down data into actionable information and create guided instructions to accomplish a specific task.
  • Reasoning- Choosing the correct algorithm is imperative in order to reach the desired outcome. It’s a matter of flow and interaction within each flow.
  • Self-Correction- Algorithms constantly go through fine-tuning to ensure it produces the most accurate results.

Machine Learning Vs. Deep Learning

Although “deep learning” and “machine learning” are frequently used interchangeably in discussions of AI, they should not be. Machine learning, which includes deep learning, is a branch of artificial intelligence.

 

Machine Learning

Algorithms trained on data are the foundation of machine learning, a type of artificial intelligence. Instead of receiving explicit programming instructions, these algorithms can learn by processing data and experience to recognize patterns and make predictions and suggestions. The algorithms adapt in response to new information and experiences to become more successful over time. Machine learning has both more promise and a greater necessity due to the volume and complexity of data currently being produced—a volume and complexity that is too great for humans to comprehend adequately. Machine learning has impacted many industries since its general use in the 1970s, leading to advancements in high-resolution weather forecasting and medical image analysis.

 

Deep Learning

Compared to classical machine learning, deep learning can process a wider variety of data sources (such as images in addition to text), requires even less human intervention, and frequently yields more accurate findings. Deep learning processes data by using neural networks, which are modeled after the way neurons interact in the human brain, through a series of iterations to learn the ever more complicated properties of the input. The neural network can then draw conclusions from the data, determine whether a decision is accurate, and apply what it has learned to conclude new data. For instance, once it “learns” the appearance of an object, it can identify it in a fresh image.

In machine learning, there are three different artificial neural network types:

  • Feed-forward neural networks: Simple neural networks called “feed-forward neural networks” transmit data from the model’s input layer to its output layer without ever going back to be reanalyzed. They were initially proposed in 1958. This is employed by the banking sector, among others, to identify fraudulent financial activities. Using a data set used to categorize transactions as fraudulent or not manually, a model is trained to predict whether a trade is fraudulent. The model is then used to determine whether new, incoming transactions are fraudulent so they can be reported for further investigation or completely prohibited.
  • Convolutional neural networks (CNNs): A sort of feed-forward neural network called a CNN is based on the visual brain of animals. They are helpful for perceptual tasks like recognizing bird or plant species from photos and for professional use cases like identifying a corporate logo in social media or diagnosing disorders from medical scans.
    • Here is how CNNs function: In the beginning, CNN gets an image—for instance, of the letter “A”—which it analyses as a group of pixels. The CNN spots distinctive elements in the concealed layers, such as the several lines that make up the letter “A.” The CNN can now designate a different image as the letter “A” if it determines that the image possesses the distinct characteristics previously recognized as constituting the letter.
  • Recurrent neural networks (RNNs): RNNs are artificial neural networks with loops in their connections, meaning the model moves data ahead while looping it back to run through earlier layers. When analyzing a sizable sample of text, speech, or images, RNNs help predict a sentiment or the conclusion of a series. They can achieve this because each input is supplied into the model separately and in conjunction with the inputs before it. RNNs can detect fraudulent financial transactions in the same way as feed-forward neural networks can, but in a more complex manner, so let’s stick with the banking example. Recurrent neural networks can “learn” from an individual’s financial behavior, such as a series of transactions like a credit card history, and compare each transaction to the person’s overall record, as opposed to feed-forward neural networks, this can assist in identifying transactions that are likely to be fraudulent. It can accomplish this in addition to using the feed-forward neural network model’s general learnings. RNNs are artificial neural networks with loops in their connections, meaning the model.

The Four Types of AI

Reactive Machine

A reactive machine is limited to using its intelligence to understand and respond to the environment around it. It cannot retain memories or draw on the past to guide current decisions. This kind of AI responds consistently to the same stimuli and is more dependable and trustworthy.

Reactive Machine Examples:

  • In a chess game, Deep Blue, a supercomputer created by IBM in the 1990s, defeated Gary Kasparov, an international grandmaster. Deep Blue was limited to distinguishing the chess pieces on a board, understanding how each moves by the game’s rules, recognizing each piece’s present position, and choosing the most logical move at that specific moment. The machine wasn’t striving to place its pieces better or anticipate prospective movements from the other player. Every turn was perceived as existing independently of earlier movements and having its reality.
  • While AlphaGo from Google is unable to predict movements in the future, it depends on its neural network to analyze game developments in the present, giving it an advantage over Deep Blue in a more challenging game.
 
 
Limited Memory

Limited memory AI can keep earlier data and predictions while gathering information and weighing options, effectively going back in time to find clues about what might happen ahead. Reactive machines are more straightforward and have less potential than AI with limited memory. Limited memory AI is generated when a team regularly trains a model on how to interpret and utilize new data or when an environment is constructed that allows models to be automatically taught and refreshed.

When utilizing limited memory AI in ML, six steps must be followed:

  1. Establish training data
  2. Create the machine learning model.
  3. Ensure the model can make predictions.
  4. Ensure the model can receive human or environmental feedback.
  5. Store human and environmental feedback as data.
  6. Reiterate the steps above as a cycle.
 
 
Theory of Mind

It is only speculative to have a theory of mind. The technological and scientific advancements required to reach this advanced level of AI have yet to be attained. The idea is founded on the psychological knowledge that the thoughts and feelings of other living creatures influence one’s behavior. This would imply that AI computers may understand how people, animals, and other machines feel, make decisions through introspection and determination, and then draw their conclusions depending on the material. For AI and humans to interact in a two-way fashion, robots would need to comprehend and interpret psychological notions like “mind,” the changes of emotions during decision-making, and many other psychological ideas in real-time.

 

Self Awareness

Once the theory of mind has been developed, the last stage of AI development will be for it to become self-aware, which will probably take a very long time. Similar to humans, this kind of AI is aware of its existence and the existence and emotional states of others. Depending on what they say to them and how they say it, it would be able to understand what other people would require. The ability of human researchers to understand the fundamentals of consciousness and then figure out how to replicate it in machines is a prerequisite for AI self-awareness.

Artificial Intelligence Examples

  • Maya AI- Maya AI is a generative AI platform that provides actionable insights from internal and new data in real time. We are designed to help retail, finance, healthcare, pharmacy, technology, and other areas to help make more informed decisions by uncovering valuable insights from their data.
  • ChatGPT- An AI chatbot named ChatGPT can create written content in various formats, including essays, code, and simple question-and-answer sets. OpenAI’s ChatGPT, introduced in November 2022, is supported by a substantial language model that enables it to imitate human writing closely.
  • Google Maps- Google Maps tracks the ebb and flow of traffic and determines the shortest route by combining location data from cell phones with user-reported information on topics like construction and auto accidents.
  • Smart Assistants- Natural language processing, known as NLP, allows personal assistants like Siri, Alexa, and Cortana to understand human commands, set reminders, do internet searches, and manage users’ home lighting. These assistants are often made to adapt to the user’s preferences over time, giving them better ideas and more personalized responses.
  • Snapchat Filters- Snapchat filters use machine learning (ML) algorithms to follow user movements, isolate the subject of a photo from its background, and alter the image displayed on the screen in response to user actions.
  • Wearables- Wearable sensors and gadgets employ deep learningWearable sensors and gadgets also employ deep learning in the healthcare sector to evaluate a patient’s overall health, including their blood pressure, heart rate, and blood sugar levels. They can also identify trends in a patient’s historical medical information and use those to predict future health issues.
  • MuZero- The DeepMind computer program MuZero is a front-runner in the race to develop real artificial general intelligence. Through sheer force and playing games millions of times, it has been able to master a variety of games, including chess and a whole library of Atari games, that it has never even been taught how to play.

How can organizations scale their AI efforts from ad hoc projects to full integration?

Organizational and cultural barriers are preventing organizations from implementing AI. It will be easier for the leaders who can overcome these obstacles to seize the opportunities offered by the AI era. Businesses needing to make full use of AI are being outpaced by those in industries like auto manufacturing and financial services. Organizations can make three fundamental changes to scale up AI:

  • Move from siloed work to interdisciplinary collaboration
    AI initiatives shouldn’t be restricted to specific groups of organizations. Instead, multiple teams with diverse talents should employ AI to help guarantee that it addresses broad business needs
  • Empower frontline data-based decision-making. At all organizational levels, AI has the potential to enable quicker, more accurate judgments. Employees must trust the algorithm’s recommendations and feel empowered to act on them to put this into practice.
  • Adopt and bolster an agile mindset. Employees that adopt an agile test-and-learn philosophy may see mistakes as opportunities for learning, which reduces risk aversion and speeds up development.

AI Benefits, Challenges, and Future

Artificial Intelligence Benefits
AI has a variety of applications, including speeding up vaccine research and automating fraud detection. Data from CB Insights show that funding for AI startups increased by more than thrice to $66.8 billion in 2022. Due to its quick acceptance, artificial intelligence (AI) is creating a stir in several businesses.

More Secure Banking
Business Insider Intelligence found that more than half of financial services companies now use AI technologies for risk management and revenue generation in its 2022 research on AI in banking. Applying AI in banking could save up to $400 billion.

Better Healthcare
In terms of medicine, a 2021 World Health Organisation research said that while implementing AI in the healthcare industry presents difficulties, the technology “holds great promise” as it may result in advantages such as better health policy and more accurate patient diagnosis.

Cutting-edge Media
AI has also impacted the entertainment industry. According to Grand View Research, the global market for AI in media and entertainment will increase from $10.87 billion in 2021 to $99.48 billion by 2030. AI applications like detecting plagiarism and creating high-definition visuals are included in that extension.

AI’s difficulties and limitations
Although AI is undoubtedly seen as a valuable and rapidly developing asset, this young area has drawbacks.In 2021, the Pew Research Centre polled 10,260 Americans about their views on AI. According to the findings, 37% of respondents are more concerned than excited, while 45% are both excited and concerned. Furthermore, more than 40% of respondents believed driverless automobiles would harm society. Even still, more respondents to the survey (almost 40%) thought it was a good idea to use AI to track the spread of incorrect information on social media.
AI is a godsend since it boosts production while lowering the possibility of human error, and efficiency. Some aspects, such as the cost of development and the likelihood that robots would replace humans in certain vocations, might be improved. It’s important to remember, though, that the artificial intelligence sector has the potential to provide a variety of occupations, some of which have yet to be imagined.

Artificial Intelligence’s Future
When considering the computational costs and the technical data infrastructure supporting artificial intelligence, putting AI into practice is a complex and expensive. According to several experts, Moore’s Law has had a significant impact on current AI approaches, and without it, deep learning wouldn’t be feasible from a financial standpoint until the 2020s. Artificial intelligence has significantly advanced several industries over the past few years. Over the coming decades, there is a strong possibility for an even more significant influence.

Relevance of Artificial Intelligence Today

Okay, now we know what AI is and how it is processed, but why are we talking about it? Simply because it makes our lives better. If you are still not convinced, we have rounded up 5 points proving why AI is here to stay.

  • Automation-AI is ideal for repetitive or detail-oriented tasks; when done manually, these tasks are time-consuming and physically (sometimes mentally, too) draining.
  • Enhancement– AI can make anything better, more innovative, and more effective. Chatbots can now hold a conversation for longer, product recommendations have gotten better.
  • Analysis– AI is great at analyzing large amounts of data in short periods. It is also great at identifying patterns.
  • Accuracy-With AI, results are accurately provided its feed with appropriate information that results in better decisions. Expect almost error-free results every time, all the time.
  • ROI-Businesses constantly dealing with complex data can scale, all thanks to AI. Now you can spend more time focusing on your business without worrying much about your operations.

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