Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that such containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) infeasible. Read More
Monthly Archives: January 2021
The Periodic Table of Data Science
The 150+ companies, resources, and tools that define the data science industry.
Open AI CLIP: learning visual concepts from natural language supervision
A few days ago OpenAI released 2 impressive models CLIP and DALL-E. While DALL-E is able to generate text from images, CLIP classifies a very wide range of images by turning image classification into a text similarity problem. The issue with current image classification networks is that they are trained on a fixed number of categories, CLIP doesn’t work this way, it learns directly from the raw text about images, and thus it isn’t limited by labels and supervision. This is quite impressive, CLIP can classify images with state of the art accuracy without any dataset-specific training. Read More
Machine learning at the speed of light: New paper demonstrates use of photonic structures for AI
As we enter the next chapter of the digital age, data traffic continues to grow exponentially. To further enhance artificial intelligence and machine learning, computers will need the ability to process vast amounts of data as quickly and as efficiently as possible.
Conventional computing methods are not up to the task, but in looking for a solution, researchers have seen the light—literally.
Light-based processors, called photonic processors, enable computers to complete complex calculations at incredible speeds. New research published this week in the journal Nature examines the potential of photonic processors for artificial intelligence applications. The results demonstrate for the first time that these devices can process information rapidly and in parallel, something that today’s electronic chips cannot do. Read More
Accelerating AI computing to the speed of light
Artificial intelligence and machine learning are already an integral part of our everyday lives online. … As the demands for AI online continue to grow, so does the need to speed up AI performance and find ways to reduce its energy consumption. Now a team of researchers has come up with a system that could help: an optical computing core prototype that uses phase-change material. This system is fast, energy efficient and capable of accelerating the neural networks used in AI and machine learning. The technology is also scalable and directly applicable to cloud computing. The team published these findings Jan. 4 in Nature Communications. Read More
2020 in Review: 10 AI Failures
This is the fourth Synced year-end compilation of “Artificial Intelligence Failures.” Our aim is not to shame nor downplay AI research, but to look at where and how it has gone awry with the hope that we can create better AI systems in the future. Read More
Expert Predictions for AI and ML in 2021
Advances in artificial intelligence (AI) go beyond algorithms—they also include associated methods to aggregate and parse new sources of data and use it to develop new applications in an expanding range of industries. We spoke to subject matter specialists and industry experts to learn about the breakthroughs expected in each of these aspects of AI in 2021. Read More
Light-carrying chips advance machine learning
Researchers found that so-called photonic processors, with which data is processed by means of light, can process information very much more rapidly and in parallel than electronic chips. Read More
How to (not) write an AI pitch
These are exciting times for the artificial intelligence community. Interest in the field is growing at an accelerating pace, registration at academic and professional machine learning courses is soaring, attendance in AI conferences is at an all-time high, and AI algorithms have become a vital component of many applications we use every day.
But as with any field going through the hype cycle, AI is surrounded by a saturation of information, much of which is misleading or of little value. … In this post, I will try to provide a few guidelines for writing good AI pitches based on my experience covering the field for several years. This is mainly a guide for the PR people who are writing AI pitches. But it should also serve reporters, who can use it to tell a good AI pitch from one that contains too much hype and too little value. Read More
The Components of a Neural Network
This article is a continuation of a series on key theoretical concepts to Machine Learning.
Neural Networks are the poster boy of Deep Learning, a section of Machine Learning characterised by its use of a large number of interwoven computations. The individual computations themselves are relatively straightforward, but it is the complexity in the connections that give them their advanced analytic ability. Read More
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