Alongside the big problem faced by the 21st century investors, especially the adults – reading comprehension, 2 more problems are becoming more and more difficult that make their lives more difficult – the status of the consensus and the growing gap between what is actually happening on Main Street and Wall Street’s interpretation. We will write a separate article about this, but, in the meantime, since the reporting season is in full swing, try to follow the reactions of the stocks against the consensus. just crazy True, the consensus consists of sell side analysts only (Sell side analysts) but it is still worth checking the current consensus compared to that of the end of 2021, the last all-time high period. In most cases it is the same people who set the consensus a year ago and this week and according to the behavior of the stocks they still have the greatest influence in the short term, we will try to explain this and also how it is related to the implementation of artificial intelligence.
Back to Skynet – artificial intelligence and learning machines are already part of the mainstream economy, a great contribution to the economy and society. There is only one big problem, there is no regulation: There are no serious companies today, in all fields of activity, that do not invest in the assimilation of artificial intelligence and machine learning in their operations, a process that was talked about a lot throughout the second half of the 20th century, but which no one dreamed would actually happen at the beginning of the 21st century. It is still not perfect, most of the companies are still in the pilot phase, but the process, according to a survey conducted by Harvard University’s Business Bulletin, is progressing and fast. By the way, the survey was conducted at the beginning of 2018 and the progress since then has been phenomenal. Not only the companies are budgeting for the assimilation, countries are also doing it and Sweden is leading before the USA and Japan.
Why do we do this? For many reasons but mainly because of the effect of intelligence on productivity, efficiency and growth. See for example the potential impact on the economy. Investors who are examining investment options in the field should take into account that artificial intelligence technologies may lead to a performance gap between companies. At one end of the spectrum, companies that fully adopt artificial intelligence tools in their organizations are expected to show significantly greater growth, efficiency and profitability compared to companies that adopt in parts and over time. Many experts claim that this is actually the “fifth technological revolution”.
The size of the global artificial intelligence market was estimated at $93.5 billion in 2021 but is expected to expand at an annual growth rate of 38.1% from 2022 to 2030. The McKinsey consulting firm estimates that artificial intelligence may provide an additional economic output of approximately $13 trillion by 2030, increasing Global GDP by about 1.2% per year. This will come mainly from the replacement of work with automation and increased innovation in products and services. The ongoing research and innovation led by the technology giants is driving the adoption of advanced technologies in industrial sectors, such as automotive, real estate, healthcare, retail, finance and manufacturing. For example, until November 2020 Intel (NYSE:INTC) acquired over 50 companies in the field and then acquired the Israeli company Cnvrg.io which develops and operates a platform for data scientists to build and run machine learning models. Intel is not alone, the technology giants led by IBM, NVDA, Facebook and Amazon, each in their own niche, are buying everything that moves in the field and the situation is similar even outside the technological environment, artificial intelligence has brought technology to the center of all organizations.
The machines, “under supervision” understood, as it turns out so far, learn fast. Let’s hope that the developers and regulators will make sure that the learning platforms and machines do not reach the level of human sophistication, develop self-awareness and start to suspect that we are using them too much. Remember Skynet? Yes, the smart system in the movie “The Terminator”, built (in the movie) for the Strategic Air Command and the North American Air and Space Defense Command, to ensure the USA ultimate defense. Skynet, “with” the help of artificial intelligence and machine learning, gained self-awareness and came to the conclusion that humans are trying neutralize it, which caused it to respond with a nuclear attack, an event that the human race in the future will refer to as “Doomsday”. As a devout follower of the development of the technology revolution, I do not think that the world of data research (Data Science), which aims to derive insights from data, has succeeded in reaching a level that allows The intelligence systems have “self-awareness”, but the words are said with caution because it is definitely possible that in the open labs in the security systems and in the giant companies, progress is being made in that direction and quickly.
The image we have attached is from the article, “Artificial intelligence vs. machine learning, let’s sort out the terms” which is worth reading. By the way, Naya College, located in Herzliya Open, is part of NAYA Technologies, which provides consulting and implementation services in the field of data platforms. In 2020, the company was merged into EPAM (NYSE:EPAM), one of the leading and most successful Professional Services companies in the world, which we previously wrote about and the founder Arkady Dobkin from Belarus. Count a dizzying success that we have been following since the issue. We will get to it again later, but first we will try to understand what it is about. What you see in the picture is the dismantling of a Russian matryoshka doll (a series of hollow dolls placed inside each other).
The term “Data Science” represents an interdisciplinary subject that tries to extract knowledge and insights from the unimaginable amount of data that has been reaching the industry since the great merger between the information revolution and the mainstream economy began. It is a science that uses techniques and theories drawn from many fields in the context of mathematics, statistics, computer science, information science and understanding the field.
The great computer and data scientist, Jim Gray, claimed that data science is the “fourth paradigm (worldview that dominates at a given time)” of science (after the empirical, theoretical, and computational paradigms), and claimed that all of science is changing because of the impact of information technology and the ever-increasing deluge of data the company and the industry (by the way, Gray disappeared with his ship in 2012 and the amount of information then, compared to today, was small).
Data scientists generate programming codes and combine them with statistical knowledge to generate insights from data. The progress in mining and working with data alongside the growing understanding that with the help of technological progress, especially the progress in the chip industry, makes it possible to reach a situation where a machine is able to simulate and display behavior derived from human thinking. That is, to make decisions and solve problems alone.
These are technologies that are able to perform specific tasks, like humans and even better. Things of the kind that, for example, the Pinterest platform manages to do with the photos and what Facebook does in facial recognition, not to mention the autonomous vehicle. By the way, these developments work wonders in the military field and no less in the agricultural field (we will write an article about this soon). What actually led the merging of data science and especially intelligence and machine learning into the industry is the development in the field of processors and especially the graphics processor, the GPU which in fact leads the introduction of deep learning, an advanced stage in machine learning based on artificial neural networks that already reminds of Skynet .
The act of assimilating the intelligence is usually done by companies like EPAM, a company that was established to help companies to assimilate these magic technologies and not lag behind the technological progress. The company provides digital platform engineering and software development services worldwide, engineering services, including requirements analysis and platform selection, customization, migration between platforms, assimilation and integration; Infrastructure management services, such as software development, testing and maintenance with private, public and mobile infrastructures for applications, databases, networks, servers, storage and system operation management including maintenance and support services.
It also provides operations solutions that include integrated engineering methods and intelligent automation and optimization solutions that include software application testing, test management, automation and consulting services to enable customers to improve their existing software testing and quality assurance practices, as well as other testing services that identify threats and close vulnerabilities to protect systems the business of its customers against loss of information. In addition, the company offers program management, as well as physical product development, such as artificial intelligence, robotics and virtual reality.
From a business point of view, the company is growing rapidly, doubling revenues and profitability in the last four years and the analysts are united in recommending a buy as they were when the stock stood at $725 and gave the company an insane value by any measure, including the future potential of $41.5 billion. The stock, which between March 2020 and the end of 2021 rose by 353%, to 725 dollars, fell at the beginning of the year by 77% to 168 dollars but has since returned by about 90% to around 315 dollars. the truth? The current price is also not the most sane, but $168 that was at the beginning of the year? The consensus currently shows earnings per share of $12.4 for 2023 (a 44% increase compared to the estimate for 2022 which will probably show a slight decrease in earnings per share compared to 2021). It is recommended to read the company’s success story, as well as the founder’s personal story.
How do you invest? Should you purchase EPAM? Maybe, but there is a small “problem” here, the competition. Companies like IBM, Accenture (NYSE:ACN), Gartner (NYSE:IT), Cognizant (NYSE:CTCH) and even Amdocs (NYSE:DOX) flock to bite into the developing niche like bees in the blooming season and what is no less important is the fact that other companies, especially giants technology, enter the field and invest huge capital in establishing dedicated divisions for the subject. The Intel we mentioned, AMZN, is acquiring the network of community clinics ONE MEDICAL (NYSE: ONEM) run by Dr. Amir Dan Rubin and is entering the health sector with power, which will undoubtedly require the assimilation of intelligence and machine learning. The giant companies in agriculture and security (Albit Systems are also included) are entering and more. In addition, there is the usual problem of defining the field that the investor is interested in, since the data industry consists of a variety of fields.
Wall Street is not ignored of course. More and more baskets, mainly activists, are coming to the market powered by the intelligence that selects the investments, such as the AIEQ basket that is actively managed by a proprietary and quantitative model that activates artificial intelligence that selects American stocks with the highest probability of growth during the next 12 months. There are many such baskets, most of which are still small. You can read about the subject in the following article.
As for investing in the field, the experts differ in their opinions, mainly due to the complexity and many variables regarding the future. Some recommend focusing on individual leading companies such as NVDA, GOOG, AMZN, IBM, FB, MSFT, PLTR, INTC, C3.ai (NYSE:AI) and SentinelOne (NYSE:S) founded and led by the talented Israeli entrepreneur Tomer Weingarten. Other experts prefer the AI & Robotics baskets, the largest of which is the BOTZ basket and there are (the majority) who prefer the large technology and IT baskets (also due to the recent declines) of the XLK or VGT type that include most of the important players. We also prefer the “big ones” and especially RYT which is based on an equal weight of the included companies.
Investors should know that investing in the field requires time and research. All the information is online and easy to obtain, therefore it is worth doing independent research and reaching an independent conclusion and most importantly, make sure that you are investing money that is intended for investment and not for current expenses.
* The above should not be seen as a recommendation to carry out operations and/or investment advice and/or investment marketing and/or advice of any kind. The information presented is for information only and is not a substitute for advice that takes into account the data and needs of each person. The one who makes use of the above information – does so at his own discretion and sole responsibility. The authors may hold some of the papers mentioned above.