Artificial Intelligence

According to International Data Corporation, revenues for the artificial intelligence (AI) market in 2021 are expected to exhibit a five-year compound annual growth rate (CAGR) of 17.5% with total revenues reaching $554.3 billion.  In contrast, according to Grand View Research, the global artificial intelligence market size was valued at USD 62.35 billion in 2020 and was projected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028.  Some of the differences in these numbers, with the market forecast going drastically down in such a short time frame, maybe explained by advanced use cases that cannot be accomplished without significant leaps in technology.  Despite the continuous research and innovation from tech giants, such as Amazon, Google and IBM, the barriers and the gap still exist.  Many of the efforts to drive the adoption of AI technology into the automotive, healthcare, retail, finance, and manufacturing industries have been crippled by these problems.  As a result, the projected revenue is not being generated, significantly cooling expectations.

AI History

The best path to understanding the up and down nature of the AI Industry is to first examine its chaotic history.  The use of computer programming to simulate human behavior was first theorized back in 1949.  The term “artificial intelligence” was fist coined at a Dartmouth conference and then founded as an academic discipline in 1956.  This was the first golden age where AI enjoyed government funding to pursue these promising logic-based problem-solving techniques.  

This lasted until 1974 when a supercomputer from MIT was commissioned by the Club of Rome to calculate global sustainability.  Using over 200,000 environmental variables from population demographics, industrial growth, food production, and ecosystem limits, the primitive AI system predicted that all civilization would collapse in 2040.  The forecast was quickly discredited and the practical use of AI was called into question.  Limited computer capacity and non-credible results lead to a period of uncertainty up until 1980, this was known as the first “AI Winter” where investment and interest in AI research was greatly reduced.

The rise of knowledge-based expert systems in the 1980’s brought new successes and a change in the focus of research and funding toward what was commonly referred to as Machine Learning (ML).  Where some saw as a simple feat of computer programming, others saw it as the herald for a new age of AI.  This was an age where even basic programming logic such as IF-THEN-ELSE statements was declared AI.   During this time, the AI hype crashed back to earth as the expert systems showed their functional limitations and proved too expensive to update and maintain.  For 1987 to 1993 the second “AI Winter” occurred.

From 1993 to 2011, optimism about AI returned with new successes marked with the help of increased computational power and more data-driven solutions.  In 1997, IBM’s Deep Blue beat world champion Kasparov at chess. In 2002, Amazon started to use automated systems to provide recommendations. In 2011, Apple releases Siri and IBM Watson beats two human champions at the TV quiz show Jeopardy.  From 2012 to 2020, driven by the development of neural networks and deep learning, a new era of increased funding and optimism about the use cases for AI arose.  Large technology vendors such as the Google used the advances to develop autonomous cars and intelligent systems like AlphaGo that were capable of beating highly intelligent humans in complicated board games. 

Even with the many AI advancements over the last decade, numerous experts and scientists in the field currently believe that the industry may be entering the third “AI Winter”.   Their belief is driven by the bubble that is forming in the AI industry where huge research projects are failing to return on their investments and their promised capabilities are falling far short of expectation.  For example, Tesla just recalled 54,000 self-driving cars because they unexpectedly run stop signs.  Many are beginning to believe that human-level intelligence cannot be achieved through either machine intelligence or deep-learning and that the current industry path is incapable of delivering on the expectations that industry giants are promising.

Services and Hardware

Artificial intelligence services include installation, integration, and maintenance & support undertakings.  This segment is projected to grow at a significant rate over the forecast period.  The artificial intelligence hardware market is dominated by Graphic Processing Units (GPUs) and CPUs due to the high computing requirements needed for current AI frameworks.  The incorporation of AI into service offerings is a growing trend and is seen in such deals as Atomwise partnering with GC Pharma to offer AI-based services to help develop more effective novel hemophilia therapies.  Both the Services and Hardware sector have historically earned about a third of the market’s revenue each with hardware always expected to take the lead.

Software

Software solutions are leading the artificial intelligence market with Machine Learning (ML) accounting for more than 38% of the share of the global revenue in 2020.  ML is growing in conjunction with accessibility to historical datasets. Since data storage and recovery have become more economical, healthcare institutions, government agencies, and commercial companies are building massive repositories of unstructured data, all accessible to AI.  From historic rain trends to clinical imaging, ML can now draw on rich datasets to advance the analytical understanding of the human condition and other complex processes.  ML derived intelligence is highly marketable and can drive revenue in many businesses and industry.

Deep Learning is another AI software where Artificial Neural Networks (ANN) in combination with representation learning are used toward recognizing images, speech, signals, and written languages.   Recognition datasets are queried/added/updated/presented where learning can be either supervised, semi-supervised or unsupervised.  When Saris recognizes a request and then plays a song, this is an example of speech recognition and deep learning in a supervised setting.  Deep Learning’s revenue (software, hardware, services) was 38% of the share of the global AI revenue in 2020.  Deep Learning took off in 2016 where the largest user Facebook represented 40% of market revenue gained.   Aerospace and the defense sector also contributed to over 20% of the market revenue owing to its need to perform remote sensing, object detection/localization, and spectrogram analysis.  Deep Learning intelligence has a limited overall market due to excessive costs and limited application.

Recent Developments 

Automakers have realized the advantages of autonomous cars and have been aggressively researching and adopting this AI technology.  For instance, Audi now utilizes Deep Learning algorithms in its camera-based technology to recognize traffic signals by their characters and shapes.  The auto industry was the largest end user of Deep Learning until 2019.  In 2020, advertising and media started adopting Deep Learning and accounted for more than an 18% share of the global AI revenue.  Now, the healthcare sector is anticipated to gain a leading share of the revenue by 2028 with this segment focusing on such things as robot-assisted surgery, virtual nursing assistants and automated image screening.

Hindrances and Potential

According to Gartner, there are three primary AI market barriers.  The first barrier is skills. Business and IT leaders acknowledge that AI will change the skillsets needed to accomplish new and existing jobs. Fifty-six percent of respondents said that acquiring the necessary skills to integrate AI into everyday work tasks will be a challenge.   The second barrier is poorly defined value propositions. Forty-two percent of respondents do not fully understand AI benefits and how it needs to be used.  Quantifying the benefits of AI projects pose a major challenge for business and IT leaders.  Some benefits can be well-defined values, such as revenue increase or time savings.  Others such as customer experience are difficult to define and measure accurately. 

The third barrier is the data scope and quality derived from AI.  Successful AI initiatives depend on a large volume of data from which organizations can draw intelligence about the best response to a situation.  Organizations are becoming aware that without sufficient quality data, the likelihood of an unknown situation being encountered goes up tremendously leading AI to failure or non-response.  Some are even beginning to realize that mimicking human activity in complex situations is not just an exercise in data gathering.  To be successful, AI needs the ability to adapt to the unknown where there is no previous data.  A situation where both Machine Intelligence and Deep Learning fail miserably.

Signal Edge ASI technology takes a different approach than Machine Intelligence and Deep Learning in solving the intelligence generation problem.  By using the brain’s algorithm for Natural Intelligence, our solution produces far superior results with just a fraction of the computational requirements.  Our approach eliminates many of the barriers because it is designed to naturally integrate into human activity, the benefits are both apparent and far-reaching, and ASI will be able to perform human-level decision quality even where no previous data exists.  The AI industry’s weaknesses and shortcomings are where ASI technology soars and where we expect high product acceptance and domination of any market segment that we choose.