Definition of Artificial Intelligence (AI)
Although several definitions of artificial intelligence (AI) have emerged in the last decades, one of the closest to what I consider AI to be is that of John McCarthy, which I will quote verbatim:
"It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI need not be limited to methods that are biologically observable."
John McCarthy
History of AI
However, this definition is not complete, nor would it be fully understood, without going back in time. When in 1950 Alan Turing published his paper "Computing Machinery and Intelligence". In it, Turing, who is known as the "father of computer science", asked the question: "Can machines think? To which he answers, with what is now widely used in computer science and is known as the "Turing Test". In this test a human interrogator would try to distinguish between a text response issued by a human or by a computer. While this test has come under a great deal of scrutiny since its publication, it remains an important part of the history of AI, as well as an ongoing concept within philosophy as it uses ideas around linguistics.
Later Stuart Russell and Peter Norvig published Artificial Intelligence: A Modern Approach, becoming one of the leading textbooks in the study of AI. In it, they delve into four possible goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking versus acting. These 4 approaches are:
Human approach:
Systems that think like humans
Systems that act like humans
Ideal approach:
Systems that think rationally
Systems that act rationally
At this point, Alan Turing's definition would have fallen into the category of "systems that act like humans".
So, what is Artificial Intelligence?
In its simplest form, artificial intelligence is a field that combines computer science and robust data sets to enable problem solving. It also encompasses subfields of machine learning and deep learning, which are often mentioned in conjunction with artificial intelligence. These disciplines are composed of AI algorithms that seek to create expert systems that make predictions or classifications based on input data.
Today, there is still a lot of buzz around the development of AI, which is expected of any new emerging technology in the market. As noted in Gartner's hype cycle , product innovations, such as autonomous cars and personal assistants, follow "a typical progression of innovation, from over-enthusiasm through a period of disillusionment to a final realization of the relevance and role of the innovation."
Types of artificial intelligence
Weak AI
Weak AI, also called narrow AI or Artificial Narrow Intelligence (ANI), is AI trained and focused to perform specific tasks. Weak AI drives most of the AI around us today. 'Narrow' might be a more accurate description for this type of AI, as it is anything but weak. In fact it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM's Watson, autonomous vehicles, etc.
Strong AI
Strong AI is composed of Artificial General Intelligence (AGI) and Super Artificial Intelligence (ASI). Artificial General Intelligence (AGI), or general AI, is a theoretical form of AI in which a machine would have intelligence equal to that of humans. That is, it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial superintelligence (ASI), also known as superintelligence, would surpass the intelligence and capability of the human brain. While strong AI remains entirely theoretical and has no practical examples in use today. That doesn't mean AI researchers aren't exploring its development. In the meantime, the best examples of ASI may come from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.
Deep Learning vs Machine Learning
Since Deep Learning and Machine Learning tend to be used interchangeably, it is worth noting the nuances between the two. As mentioned earlier, both deep learning and machine learning are subfields of artificial intelligence. In fact, deep learning is actually a subfield of machine learning.
Deep learning is actually composed of neural networks. The term "Deep" within the concept of deep learning refers to a neural network composed of more than three layers, which would include inputs and output, and can be considered a deep learning algorithm.
The difference between the two types of learning (deep vs. automatic) is in the way each algorithm learns. Deep learning automates much of the feature extraction part of the process, which eliminates some of the manual human intervention required. Allowing the use of larger data sets. You can think of deep learning as "scalable machine learning," as Lex Fridman pointed out in a lecture at MIT. Classical, or "non-deep" machine learning relies more on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs. Which generally requires more structured data to learn.
"Deep" machine learning can leverage labeled data sets, also known as supervised learning, to inform its algorithm, but does not necessarily require a labeled data set. It can ingest unstructured data in its original form (e.g., text, images). And it can itself automatically determine/assign the hierarchy of features that distinguish different categories of data from each other. Unlike machine learning, it does not require human intervention to process the data. Which allows us to scale machine learning in more interesting ways.
Artificial intelligence applications
There are numerous real-world applications of AI systems today. Below are some of the most common examples:
Speech recognition: also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text conversion. And it is a capability that uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to perform voice searches. For example, Siri.
Customer service: online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) on topics, such as shipping, or provide personalized advice, cross-sell products or size suggestions for users, changing the way we think about customer engagement on websites and social media platforms. Examples include messaging bots (chatbots) on e-commerce sites with virtual agents, messaging apps such as Slack and Facebook Messenger, and tasks typically performed by virtual assistants and voice assistants.
Computer vision: This artificial intelligence technology enables computers and systems to obtain meaningful information from digital images, videos and other visual inputs. Based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Driven by convolutional neural networks, computer vision has applications within facial recognition/license plate recognition,/objects for photo tagging in databases/social networks, radiology image recognition in healthcare, and autonomous cars within the automotive industry.
Recommendation engines: using past consumer behavior data, AI algorithms can help uncover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
Automated trading bots: designed to optimize investors' asset portfolios, AI-powered high-frequency trading platforms perform thousands or even millions of trades per day without any human intervention.
What is driving the adoption of AI?
AI until recently seemed like something of the future, even though as we have seen above it is something that, at least theoretically, is already several decades old. In fact its adoption is being so subtle that sometimes we are not really aware of its integration into multiple facets of our lives. And these are mainly due to three factors that are driving the development of AI across industries:
Affordable, high-performance computing capability is already available. The abundance of commodity computing power in the cloud enables easy access to affordable, high-performance computing power. Prior to this development, the only computing environments available for AI were not cloud-based and were cost prohibitive.
Large volumes of data are available for training. AI must be trained on lots of data to make the right predictions. The emergence of different tools for labeling data, plus the ease and affordability with which organizations can store and process structured and unstructured data, allows more organizations to design and train AI algorithms.
Applied AI provides a competitive advantage. Increasingly, companies are recognizing the competitive advantage of applying AI insights to business objectives and making it an enterprise-wide priority. For example, targeted recommendations provided by AI can help companies make better decisions faster. Many AI features and capabilities can reduce costs and risks, speed time to market, and much more.
What can we expect from AI in the near term?
AI is a strategic imperative for any company that wants to gain greater efficiency, new revenue opportunities and/or increase customer loyalty. As you can understand it is fast becoming a competitive advantage for many organizations that are knowing how to leverage it. With AI, companies can meet more goals in less time, create personalized and engaging customer experiences, and predict business outcomes to drive greater profitability.
However, AI still remains a new and complex technology. To take full advantage of it, you need expertise in designing and managing your AI solutions in a balanced way. A successful AI project requires more than simply hiring a data scientist. Companies must implement the right tools, processes and management strategies to ensure AI success.
In short, we are still in the stone age of AI. I believe we still have a long way to go in the advancement of this amazing technology, which is very likely to bring us new paradigms in the coming decades, for which our human mind is not even ready yet.....
