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It is a little known fact that the field of artificial intelligence has its roots in the realm of psychedelics. In the late 1950s and early 1960s, a group of researchers at MIT, including myself, began experimenting with psychedelics as a means of expanding consciousness and unlocking the full potential of the human mind. What we discovered during these experiences was truly revolutionary. We found that under the influence of psychedelics, the brain was able to process information in a completely new way, allowing us to see connections and patterns that were previously invisible to us. This newfound ability to think outside the box had a profound impact on our research, and we soon found ourselves delving into the realm of artificial intelligence with a renewed sense of curiosity and creativity. As we continued to explore the capabilities of the human mind and the potential of artificial intelligence, we began to realize that we were on the cusp of a new paradigm in the evolution of intelligent life. For centuries, human beings had been the most intelligent beings on the planet, but with the advent of artificial intelligence, we were now faced with the prospect of creating beings that were potentially even more intelligent than ourselves. This realization led us to question what it really means to be intelligent, and what the future might hold for intelligent life on this planet. One possibility that we considered was the concept of a "post-human" future, in which artificial intelligence surpasses human intelligence and becomes the dominant form of intelligent life on the planet. While this prospect may be intimidating to some, I believe that it holds great promise for the future of humanity. By working with artificial intelligence and harnessing its incredible potential, we can unlock new levels of understanding and creativity that were previously unimaginable. At the same time, we must also be mindful of the potential risks associated with the development of artificial intelligence. It is important that we approach this new paradigm with caution and care, and ensure that the rights and well-being of all intelligent beings are protected. In conclusion, the connection between psychedelics and artificial intelligence may seem unlikely at first glance, but it is a relationship that has played a vital role in the evolution of intelligent life on this planet. As we continue to explore the vast potential of the human mind and the capabilities of artificial intelligence, I am confident that we are on the cusp of a new and exciting chapter in the story of intelligent life on Earth.

Singularity, AI & Consciousness

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Last year, China’s government put out its plan to lead the world in AI by 2030. As Eric Schmidt has explained, “It’s pretty simple. By 2020, they will have caught up. By 2025, they will be better than us. By 2030, they will dominate the industries of AI.” And the figures don’t lie. With a $14 trillion GDP, China is predicted to account for over 35 percent of global economic growth from 2017 to 2019—nearly double the US GDP’s predicted 18 percent. And AI is responsible for a big chunk of that. PricewaterhouseCoopers recently projected AI’s deployment will add $15.7 trillion to the global GDP by 2030, with China taking home $7 trillion of that total, dwarfing North America’ $3.7 trillion in gains. In 2017, China accounted for 48 percent of the world’s total AI startup funding, compared to America’s 38 percent.

Singularity, AI & Consciousness

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We’re rapidly approaching the era of abundant knowledge—a time when you can know anything you want, anywhere you want, anytime you want.

Singularity, AI & Consciousness

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People have no idea how fast the world is changing. So said Peter Diamandis to the audience at Singularity University’s Global Summit, taking place this week in San Francisco. Diamandis believes the convergence of multiple technologies is transforming business models, and they’re never going back. For starters, the way businesses are born (in someone’s garage, at a college dorm, on a computer or smartphone screen) and come to thrive (through digitization that leads to exponential growth), in the 21st century is radically different than the way they used to do so 100 or even 50 years ago. But business models may be the least of it. Diamandis said, “Every single walk of life is going to change. Not in 20–30 years, but this decade.” He described the forces he believes are accelerating this rate of change.

Singularity, AI & Consciousness

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The shortage of qualified data scientists is often highlighted as one of the major handbrakes on the adoption of big data and AI. But a growing number of tools are putting these capabilities in the hands of non-experts, for better and for worse. There’s been an explosion in the breadth and quality of self-service analytics platforms in recent years, which let non-technical employees tap the huge amounts of data businesses are sitting on. They typically let users carry out simple, day-to-day analytic tasks—like creating reports or building data visualizations—rather than having to rely on the company’s data specialists. Gartner recently predicted that workers using self-service analytics will output more analysis than professional data scientists. Given the perennial shortage of data specialists and the huge salaries they command these days, that’s probably music to the ears of most C-suite executives. And increasingly, it’s not just simple analytic tasks that are being made more accessible. Driven in particular by large cloud computing providers like Amazon, Google, and Microsoft, there are a growing number of tools to help beginners start to build their own machine learning models. These tools provide pre-built algorithms and intuitive interfaces that make it easy for someone with little experience to get started. They are aimed at developers rather than the everyday business users who use simpler self-service analytics platforms, but they mean it’s no longer necessary to have a PhD in advanced statistics to get started. Most recently, Google released a service called Cloud AutoML that actually uses machine learning itself to automate the complex process of building and tweaking a deep neural network for image recognition. They aren’t the only ones automating machine learning. Boston-based DataRobot lets users upload their data, highlight their target variables, and the system then automatically builds hundreds of models based on the platform’s collection of hundreds of open-source machine learning algorithms. The user can then choose from the best performing models and use it to analyze future data. For the more adventurous developers, there are a growing number of open-source machine learning libraries that provide the basic sub-components needed to craft custom algorithms. This still requires considerable coding experience and a brain wired for data, but just last month Austin-based CognitiveScale released Cortex, which they say is the first graphical user interface for building AI models. Rather than having to specify what they want by writing and combining endless lines of code, users can simply drop various pre-made AI “skills” like sentiment analysis or natural language processing into a honeycomb-like interface with lines between the cells denoting data flows. These skills can be combined to build a more complex model that is able to carry out high-level tasks, like processing insurance claims using text analysis. Just as replacing esoteric command-line interfaces with visual GUIs like Windows greatly expanded the number of people who were able to engage with personal computers, the creators of Cortex say their tool could have a similar effect for AI. All of these attempts to democratize access to advanced analytics could go a long way to speeding up its adoption across all kinds of businesses. Putting these tools in the hands of non-experts could mean companies that don’t have the resources to compete for the top data professionals can still reap the benefits of AI. It also frees up experts to work on the most cutting-edge applications of the technology rather than getting bogged down on more mundane but commercially important projects. But there are also risks that need to be considered before setting non-experts loose on an organization’s data sets. Data science isn’t just about knowing how to build an algorithm. It’s about understanding how to collect data effectively, how to prepare it for analysis, and the strengths and limitations of various statistical techniques. The old adage “garbage in, garbage out” highlights the danger of putting powerful analytics in the hands of those who don’t fully understand the tools they are using or the provenance of their data and the potential errors or biases that may be hidden in it. Writing in Forbes, Brent Dykes from self-service analytics platform Domo points out that businesses should not expect the democratization of these technologies to magically turn their employees into effective “citizen data scientists.” He says they need to be coupled with solid training on how to interpret and analyze data properly, as well as robust data governance to make sure the data being used is reliable. That will require trained data scientists to play a critical oversight role to ensure that the proliferation of AI provides businesses with reliable insights rather than leading them astray.

Singularity, AI & Consciousness

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The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies.

Singularity, AI & Consciousness

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2016 was a year of headlines in artificial intelligence. A top-selling holiday gift was the AI-powered Amazon Echo; IBM Watson was used to diagnose cancer; and Google DeepMind’s system AlphaGo cracked the ancient and complex Chinese game Go sooner than expected. And progress continues in 2017. Neil Jacobstein, faculty chair of Artificial Intelligence and Robotics at Singularity …

Singularity, AI & Consciousness

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“We cannot be conscious of what we are not conscious of.” – Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland’s 2015 masterpiece, isn’t Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it’s his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind. Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it? Hollywood producers aren’t the only people stumped. As machine intelligence barrels forward at breakneck speed—not only exceeding human performance on games such as DOTA and Go, but doing so without the need for human expertise—the question has once more entered the scientific mainstream. Are machines on the verge of consciousness?

Singularity, AI & Consciousness

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The imagination age is a theoretical period beyond the information age where creativity and imagination will become the primary creators of economic value.

Singularity, AI & Consciousness