IBM is an information technology company that Chinese are very familiar with. It not only dominated the history of the computer industry before the advent of the PC, but also created the PC industry by itself. Although in crisis in the 1980s, ibm managed to return to the position of A-list giant under the leadership of Louis Gerstner from the 1990s (though still far behind a new generation of companies like Microsoft). IBM's investment in artificial intelligence (AI) dates back to the 1990s, and many still remember reports of "deep blue" supercomputers beating humans in chess. In the 2010s, IBM launched Watson, an "artificial intelligence solution based on natural semantics", in an attempt to use this as a fulcrum to completely transform human business and government activities.
However, in July 2022, IBM's market value was only about $110 billion, far lower than the Internet giants represented by Apple, Google, and Amazon. IBM Watson has long since been reduced to a historical relic, and what is most impressive now is the scene where Alphabet (Google)-developed Alpha Go defeats humans in Go; Alphabet, along with Amazon and Microsoft, has become the new AI Big Three (some argue that Meta is the fourth). No matter how far AI can evolve in the next two decades, IBM will be less beneficial than Alphabet' descendants.
IBM and Alphabet have many reasons for this in the field of artificial intelligence, but the most fundamental reason is that the former has not found the most suitable and cost-effective application scenario, while the latter has found. As a result, Alphabet is creating a virtuous circle between basic R&D and application along the path of least resistance; IBM's blockbuster gamble ended in a fiasco. "If future generations mourn and do not learn from them, they will make future generations mourn for future generations!"
It is no exaggeration to say that the more cutting-edge and ungrounded the research direction, the stronger IBM's sense of existence tends to be; If a research project is still in the lab, it is likely to be a project that IBM is proficient in. The most typical example of the moment is Quantum Computing, which is seen by academia as a potential disruptor of traditional (electronic) computers, but is still very far from large-scale practical use. IBM Q System One is not only the world's first Circuit-based quantum computer, but also the first commercially operational quantum computer. Although the actual commercial value is very limited, this achievement has still been reported by countless media and will certainly be repeatedly emphasized in the various PPT issued by IBM.
IBM Q System One, the world's first quantum circuit computer
Those familiar with history might venture to predict that once quantum computing begins to be put into practical use on a large scale, IBM's lead will be greatly reduced, if not lost. The incumbent tech company has the ability to sustain basic R&D investments, but it's always lagging behind the newer, more flexible companies in terms of finding application scenarios — formerly Microsoft, now Alphabet. We don't need to look for distant examples, just a review of IBM Watson, a big bubble that opens high and goes low.
Machine learning (ML) is an important branch of artificial intelligence and has gradually developed into an independent field of research since the 1990s. In this research report, there is no need to discuss the subtle differences between the two, just understand that machine learning pays more attention to big data and emphasizes the training of massive amounts of data to automatically improve the accuracy of algorithms. Along this path, algorithms can replace humans to solve more and more intellectual problems, just as machines have solved most of the physical problems instead of humans. It's not without reason that some people think of machine learning as the core technology of the next industrial revolution.
IBM could not have been absent from such an important cutting-edge technology. Back in 2006, it launched the Watson project with the goal of achieving complex human-machine questions and answers under natural semantics. That is, users can ask questions of computers in everyday language without having to convert them into code to get professional, understandable answers. In January-February 2011, IBM Watson made a high-profile start: "Jeopardy," the oldest television quiz show in the United States. He defeated two human champions and won a $1 million grand prize. That same year, IBM announced that Watson had reached the level of knowledge of second-year medical students.
The ambitious IBM decided to use Watson to transform healthcare, the most complex and resource-wasting industry in the entire Western world, and focused on the most difficult cancer treatment from the beginning. In 2013, three large medical institutions, including the University of Texas MD Anderson Cancer Center, signed a contract with Watson; The all-time high in IBM's market capitalization also happened to occur in this year (25% higher than it is now). In 2015, Watson Health officially became an independent division of IBM. At the same time, Watson has also been applied in education, transportation, engineering, government affairs, weather forecasting and other fields. There is reason to believe that IBM will be one of the biggest beneficiaries of the industrialization of machine learning.
However, IBM Watson's history since then gives the term "technology bubble" the best footnote. Since 2018, a large number of medical institutions have terminated their contracts with Watson every year; In 2019, IEEE Spectrum published an article detailing why Watson couldn't deliver on his promises; In 2021, IBM finally decided to sell most of Watson Health's assets, and the sale was not completed until 2022 due to flat buyer interest. Taken together, Watson's failure is entirely understandable:
- Recommendations for cancer treatment do not work well. Specifically, Watson's treatment recommendations were less consistent with those of human experts, leading many hospitals to refuse to accept them. Of course, the advice of human experts is not necessarily correct, but in such a big matter as cancer treatment, no one dares to easily use artificial intelligence to negate human experts. In addition, Watson has limited application scenarios and only has relatively high accuracy for common cancers such as lung cancer.
- It is not possible to integrate into existing medical information and data systems. At MD Anderson Cancer Center, Watson doesn't even have access to the Electronic Medical Record System; In the UK, Watson is also often unable to read actual medical records. The reason lies on the one hand in regulation (patient privacy protection) and on the other hand in the complexity of the healthcare system. Giving Watson access to all hospitals' Medical Information Systems (HIS) is simply hard to climb!
- It is not a direct substitute for the doctor's labor. Watson Health was originally designed to reduce the workload of doctors, but due to the dual reasons of technology and ethics, it cannot replace doctors to issue diagnostic opinions, but can only provide reference for doctors. As a result, in many hospitals, Watson has been reduced to an expensive "young doctor training system", and the workload of senior doctors has not been greatly alleviated.
In the final analysis, IBM's choice of medical treatment as a breakthrough would have been wrong, because medical care involves too many vested interests and too many ethical issues; Choosing cancer diagnosis and treatment as a breakthrough in market segmentation is even more wrong, because medical institutions must be very conservative about the treatment of cancer, which is a serious disease, and it is difficult to believe in artificial intelligence; Choosing to provide cancer diagnosis and treatment advice with natural semantics is even more artificially increasing the difficulty and delusional to ascend to the sky in one step. In fact, until 2020, medical institutions are still complaining about Watson's lack of comprehension of everyday languages!
IBM's journey from hope to despair is vividly reflected in its annual report: Watson was mentioned only 8 times in 2012, then soared to 131 times in 2016, and then fell all the way back to 6 times in 2021. It is worth mentioning that in 2020, Watson has a momentum of returning to the light, but this is due to the large number of individual users using Watson voice assistants during the epidemic, which has nothing to do with "tall" medical services.
If IBM had not chosen healthcare, an extremely difficult industry to transform, as a breakthrough, but from the beginning focused on education, transportation and other relatively easy to transform industries, perhaps Watson's fate would have been very different. From another perspective, can IBM make Watson a pure consumer application, such as smart home products or smart robots? After all, "natural semantic recognition" has a strong appeal to consumers, even Siri and Alexa, such as semi-hanging intelligent voice assistants, can win the favor of users, let alone the higher level of Watson? Unfortunately, history cannot be assumed. The result of being ambitious and blindly pursuing to change human history is to become the laughing stock of human history.
Unfortunately, since the sale of the PC business in 2005, IBM has basically lost its consumer-grade business, and it has lost its perception of the consumer market. As you can see, IBM almost never seriously promotes Watson in consumer industries such as retail and home appliances. In 2017, IBM acquired an advertising agency in an attempt to use Watson to direct advertising — but what advantages does IBM have over Google and Meta's highly sophisticated precision delivery technologies? Even if IBM wants to bring Watson technology to thousands of households, it must first find a To C technology giant as a partner. It is conceivable that Apple, Amazon, and Microsoft should not be very cold about this.
It can be seen that Microsoft insisted on not abandoning the consumer-grade business in the difficult period of 2007-14, but launched an impact on the intelligent hardware market again and again, and how far-sighted it was. It was the period when the gap between Microsoft and IBM's market capitalization was the smallest since the beginning of the 21st century, and it was also a period of fundamental divergence between the two companies. Incidentally, IBM may be the only company of all the information technology giants in history that has never seriously considered doing any consumer Internet business.
When IBM placed a big bet on Watson, Google's (later renamed Alphabet) advertising business was in its prime, and that golden age hadn't ended yet. In 2011, the year Watson was first made public, IBM's operating income was 2.82 times that of Google and 1.63 times its net profit; by 2021, the year IBM desperately sought to sell Watson Health, its operating income was only 27% of Alphabet's, and its net profit was only 6% of the latter.
Compared to the "hellish difficulty" of cancer treatment, Internet advertising is a "simple difficulty": in this scenario, there are far fewer regulatory and ethical issues, it is much easier to make results, and there are no large entangled vested interests. As the world's largest search engine, map and network alliance service provider, Alphabet itself controls the end consumer, which also masters the entire practical chain of machine learning technology. Advances in machine learning have improved the efficiency of advertising pushes, thereby pleasing advertisers; It also improves the accuracy of search results, thus pleasing consumers. The virtuous circle that IBM failed to achieve in medical, transportation and other scenarios was relatively smoothly realized in the advertising scene.
In January 2022, IBM finally sold most of Watson Health's assets for about $1 billion, bringing an era to an end; It also announced that it would change its strategy and henceforth focus on "hybrid cloud and AI strategy platforms." So far, there is no better option, because IBM's financial resources are no longer enough to support such a huge ambition. In the field of artificial intelligence and machine learning, Alphabet's strength in basic research and development is at least not inferior to IBM. Assuming the former wants to repeat IBM Watson's adventures in the healthcare industry, while not necessarily successful, the prospects for success are likely to be slightly greater — at least it can last a little longer.
Engels's "Speech at marx's tomb" mentions: "People must first eat, drink, live, and wear before they can engage in politics, science, art, religion, and so on; So, the direct production of material means of subsistence ... it forms the foundation. "For enterprises, basic R&D is a relatively advanced job, and it must be supported by cash flow, application scenarios and ecosystem support from the front-end business unit. This truth is very intuitive and self-explanatory, but unfortunately, not many people really understand it in the United States or China.