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As IBM Watson’s experience shows, the road to AI success is full of challenges. Yet overall, it has been a very good year for AI and the companies developing it. In a recent podcast by the BBC, Alphabet CEO Sundar Pichai says: “I see [AI] As a very profoundly capable technique. If you think about fire or electricity or the internet, it’s the same, but I think it’s more profound. “

With AI appearing in many industries, from semiconductor design to software software development to oversupply, farming, distribution, music production, and classical sculpture, this profound effect is becoming more pronounced. In all cases, AI is potentially increasing human growth while dramatically accelerating the development of the final product. In biology, laboratory work can take years to determine the structure of just one protein, but new AIs announced by Washington University can reduce this time to 10 minutes. In the sculpture example, it takes only 11 days to make a replica of the “Psych Revived by Cup Cup Pid Kiss” produced by ABB 2, an industrial robot developed by ABB Robotics, while the original 18th century sculptor Canova takes about five years. And because of the epidemic, demand for industrial robots has increased in many industries over the past year.

AI is accelerating the output

In a recent paper in the journal Nature, Google described how it has developed a deep neural network to learn reinforcement that designs computer chips faster than humans. Very fast. The paper discusses a chip design that will take engineers months and instead take less than six hours to hear with the new AI software. CNBC As noted by Google A.I. Is being used to design chips that can be used to create more sophisticated AI systems, enhancing already exponential performance through the virtue cycle of innovation.

It’s not just Google that is accelerating semiconductor chip development using AI. Chip design company Synopsi recently demonstrated how a problem that had taken months of work by the entire design team could be solved by a single engineer with excellent results in just a few weeks. These are just a few examples published in some of the recent headline stories. All the major chipmakers and semiconductor tool companies have their hands on some aspect of AI.

On the other side of computing, a similar revolution is taking place in software software for running applications. GPT– – Officially Generative Pre-Trend Transformer 3 – Developed by OpenAI The language model has proven capable of producing consistent prose from text prompts. This was created to do this, but it turns out that it can also produce other text, including computer code.

According to The Economist Story, new software software development tools based on AI can suggest context-sensitive code in-line, as much as Gmail and Outlook now suggest how to complete a line in email, or do it for word text processing. In the case of commercial systems using GPT-3, the instructions may include complete code modules to complete tasks such as creating a purchase order. This not only reduces the time it takes to develop software in advance – it also reduces the “cognitive overhead” according to one user, as choosing from the presented options is easier than developing the original code. This is similar to old school programming where code is imported from a library, although the programmer no longer needs to know anything about the library, and the process is almost completely automated.

One of the new pay generation software software development tools is CoPilot, an AI-powered programming tool developed jointly by OpenAI and GitHub, located as a growth for human programmers. The tool uses Codex, which is based on GPT-3 but fine-tuned for programming tasks. Other programmers have previously written to solve a similar problem based on the new system, which suggests blocks of code from the GitHub repository.

While there are concerns that these (and similar tools) will be developed to replace engineers and programmers, it is widely believed that there is still some time for such developments in the future. Nevertheless, these tools will accelerate the development process – in some cases, dramatically.

Ready for a productivity boom

The effect of this AI-enabled automation has begun. In the panel discussion, Sanjeev Vohra for Accenture Global Lead, Applied Intelligence, explained that he has observed “big shifts” in companies using technologies such as AI, analytics and machine learning, which are increasing revenue and efficiency. This shift will lead to a boom in productivity, according to Stanford University professor Eric Bryanz Olfsson. He said AI is already better or better than humans in certain applications and encourages businesses to focus on incorporating processing technology. That said, he does, will soon see momentum in productivity.

These examples and trends suggest that AI enters tech-mode f mode as we emerge from the economic downturn due to the epidemic. And the inclusion of labor-saving technology coming out of the recession is the standard operating operating process for many companies. However, this time the demand for auto tomtom is particularly sharp given the combination of labor shortages and wage growth. Thanks to the availability of mature labor-saving technologies, we have already seen companies work more with fewer people in the last two years.

So far, the AIA has not had a major impact on employment. But if Vohra and Brianj Olfanson are right, this is starting to change. The timing coincides with a study by PWC that describes three overlapping cycles of extended auto tommation in the 2030s, each with their own degree of job impact. This is the wave wave, growth wave and autonomy wave of the cycle algorithm. According to a PWC report, there is a risk of only 3% of jobs for auto tomtation from the algorithm wave in the early 2020s, but this increase is expected to be around 20% by the end of the 2020s and 30% by mid-2020s. 2030s.

It could be that the frightened robot Apocalypse has jumped-started due to the COVID-19 epidemic. Understandably, it is worrying that AI-powered automation will eliminate jobs. However, the tendency to resist technological advances is unlikely to succeed, especially since competitive pressures lead to intact automation. “We will continue to automate the look at as many jobs and parts of the organization as possible,” said Lauren Knudson of Broadcom. And while it’s possible that attitudes toward AI may change, a recent survey suggests that% of office workers really want more AI to help with their day-to-day work.

As Noah Smith, an assistant professor of finance at Stony Brook University, argues in a .p-ed, the best way to alleviate AI concerns and enable further technological advancement is in the field of public policy. He particularly noted the need for national health insurance, job search assistance and greater equality of income. Clearly, the pace of AI adoption and concomitant automation will put more pressure on public policy decision makers. We will all learn to adapt over time. As President Kennedy put it in his 1963 speech at the Assembly Hall in Frankfurt’s Skelskirche, “Change is the rule of life. And those who only look at the past or the present are sure to miss the future. “

Gary Grossman is Senior VP of Technology Practice at Edelman and Edelman is the Global Lead of the AI ​​Center Excel f Excellence.


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