The Trump administration is prioritizing the critical role of artificial intelligence in creating and upholding freedom. Just three weeks in, Vice President JD Vance declared at a global AI summit in Paris that AI "will make people more productive, more prosperous, and more free. The United States of America is the leader in AI, and our administration plans to keep it that way." To achieve this, the White House is working toward an AI action plan and calling on leading American AI companies to submit our best ideas. OpenAI is pleased to submit proposals today on a range of important considerations for AI from national security, to infrastructure and energy, to the federal government's own use of AI.

Just last week Rich Sutton published a very short blog post titled The Bitter Lesson. I'm going to try to keep this review shorter than his post. Sutton is well known for his long and sustained contributions to reinforcement learning. In his post he argues, using many good examples, that over the 70 year history of AI, more computation and less built in knowledge has always won out as the best way to build Artificial Intelligence systems. This resonates with a current mode of thinking among many of the newer entrants to AI that it is better to design learning networks and put in massive amounts of computer power, than to try to design a structure for computation that is specialized in any way for the task.

In a popular blog post titled "The Bitter Lesson," Richard Sutton argues that AI's progress has resulted from cheaper computation, not human design decisions based on problem-specific information. Sutton diminishes researchers that build knowledge into solutions based on their understanding of a problem to improve performance. This temptation, Sutton explains, is good for short-term performance gains, and such vanity is satisfying to the researcher. However, such human ingenuity comes at the expense of AI's divine destiny by inhibiting the development of a solution that doesn't want our help understanding a problem. AI's goal is to recreate the problem-solver ex nihilo, not to solve problems directly.[1]

The personal computer's impact on productivity and augmenting human ingenuity is very similar to AI's impact today. The Internet -- Suddenly, from any PC, you could access so many resources, providing so much intelligence across this connection. The Smartphone -- This device provides mobility, building on the power of the internet. Cloud and cloud computing -- The cloud was critical for transformational technology, allowing users to build on top of basic innovation. The cloud provides storage and allows companies the flexibility of being able to train their models and use the computing they need to train those models.

Life and annuity insurers have adopted a host of new technologies in recent years to drive growth. Artificial intelligence, robotics process automation and data analytics – are only a few of the many new technologies that underwriters have at their disposal today. Insurers are putting all the parts into place for underwriters to improve efficiency and drive new growth -- but they aren't quite there yet. Recent Accenture research found that underwriters feel positive about these new tools but are also concerned about their ability to use them. To realize the full value of their tech investments, insurers must invest not only in technology but also in their workforces to ensure they can use these new technologies properly.

As I explain in The Evolution of Evolution, when humanity merely evolves downstream, from an existing normalization of truth, get ready for infinite regression. That is my stern warning to Dutch funds, APG and PGGM, promoting an AI-driven investment platform for asset-owners, as reported by top1000funds. As the publication states: "The AI-driven technology sifts through reams of structured and unstructured data to gauge the extent to which companies' products and activities meet the UN's Sustainable Development Goals (SDGs)." Let's break this smorgasbord of misplaced assumptions in asset management down once again: The U.N. is completely wrong in its reference and association with sustainability, an evolutionary oxymoron, for sustainability does not exist anywhere in the universe. That misnomer is more than a mere conflict of surface-level semantics as it involves a fundamental misapprehension of the cosmos and the role of humanity on this tiny little planet in it. Those who refer to sustainability are new-born flat-earthers, missing a few crucial dimensions in the fractal of human expansion.

But we humans are still smarter. We are now at a point with artificial intelligence (AI) and machine learning (ML) where we can use a new confluence of forces to increase human productivity and ingenuity. All the while, we must remember why we're using these new tools and how they can help us work smarter and faster. If you saw the movie Aliens, you might remember the iconic image of Ripley encased in a mechanical exoskeleton, ready to take on the deadly alien queen. AI's impact on human intelligence is akin to a mechanical exoskeleton on the human body.

But we humans are still smarter. We are now at a point with artificial intelligence (AI) and machine learning (ML) where we can use a new confluence of forces to increase human productivity and ingenuity. All the while, we must remember why we're using these new tools and how they can help us work smarter and faster. If you saw the movie Aliens, you might remember the iconic image of Ripley encased in a mechanical exoskeleton, ready to take on the deadly alien queen. AI's impact on human intelligence is akin to a mechanical exoskeleton on the human body.

Artificial intelligence to many people may seem like a remote idea swirling in the realm of technology eggheads, but it actually is being adapted to many practical areas more familiar to the public. For example, the digital technology is helping protect the priceless ancient Buddhist relics of Dunhuang, improving electricity networks and assisting malaria medical research. The World Artificial Intelligence Conference 2019, which opens on Thursday in Shanghai, won't be focused only on jargon like "algorithms" and "adversarial networks." It will be discussing the ways in which artificial intelligence (AI) is being integrated in daily life, in a range of industries and in the transformation of the economy. The conference also reinforces Shanghai's leading role in the new technology, amid plans to turn the city into an AI global hub of the future.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding artificial intelligence. In 1956, researchers at Dartmouth College coined the term "artificial intelligence," a field of science that aims to enable machines to replicate the capabilities of the human mind. AI pioneers believed at the time that in short time, "machines will be capable… of doing any work a man can do." For decades, AI scientists and researchers have been trying to recreate the logic and functionalities of the human brain. And for decades, they have dismayed themselves and the general public.