In recent years, electronics engineers have been trying to develop new brain-inspired hardware that can run artificial intelligence (AI) models more efficiently. While most existing hardware is specialized in either sensing, processing or storing data, some teams have been exploring the possibility of combining these three functionalities in a single device.
Researchers at Xi’an Jiaotong University, the University of Hong Kong and Xi’an University of Science and Technology introduced a new organic transistor that can act as a sensor and processor. This transistor, introduced in a paper published in Nature Electronics, is based on a vertical traverse architecture and a crystalline-amorphous channel that can be selectively doped by ions, allowing it to switch between two reconfigurable modes.
“Conventional artificial intelligence (AI) hardware uses separate systems for data sensing, processing, and memory storage,” Prof. Wei Ma and Prof. Zhongrui Wang, two of the researchers who carried out the study, told Tech Xplore.
The mammalian retina is a complex system consisting out of cones (for color) and rods (for peripheral monochrome) that provide the raw image data which is then processed into successive layers of neurons before this preprocessed data is sent via the optical nerve to the brain’s visual cortex. In order to emulate this system as closely as possible, researchers at Penn State University have created a system that uses perovskite (methylammonium lead bromide, MAPbX3) RGB photodetectors and a neuromorphic processing algorithm that performs similar processing as the biological retina.
Panchromatic imaging is defined as being ‘sensitive to light of all colors in the visible spectrum’, which in imaging means enhancing the monochromatic (e.g. RGB) channels using panchromatic (intensity, not frequency) data. For the retina this means that the incoming light is not merely used to determine the separate colors, but also the intensity, which is what underlies the wide dynamic range of the Mark I eyeball. In this experiment, layers of these MAPbX3 (X being Cl, Br, I or combination thereof) perovskites formed stacked RGB sensors.
The output of these sensor layers was then processed in a pretrained convolutional neural network, to generate the final, panchromatic image which could then be used for a wide range of purposes. Some applications noted by the researchers include new types of digital cameras, as well as artificial retinas, limited mostly by how well the perovskite layers scale in resolution, and their longevity, which is a long-standing issue with perovskites. Another possibility raised is that of powering at least part of the system using the energy collected by the perovskite layers, akin to proposed perovskite-based solar panels.
AI is everywhere. Its use is being debated in headlines, on social media and around dinner tables. To some, the rate of AI acceleration is concerning, with many technology leaders calling for a six-month pause in the training of new systems to better understand the impact such tools are having. To others, AI is seen as the cornerstone of the fourth industrial revolution, the latest disruptive technology opening up possibilities for new ways of learning, working and living that we have never experienced before.
Yet, disruptive technologies are nothing new. They have been changing the way we live and work for decades. And these changes have not been without consequences, particularly in the form of economic dislocation and social upheaval. Automation in manufacturing has streamlined mass production and driven down costs; Ecommerce platforms have reshaped the way we shop and do business; even online education has found new ways to provide flexible and affordable ways of learning, delivering opportunities to millions across the globe that simply were not available before.
Presently, much of the discussion around the impact of AI is based on conjecture. However, it is widely agreed that it will have a major impact on jobs and even has the potential to call into question the very fundamentals of what work is. What is not understood is how AI will play out across society in the longer term. Will it, like previous technological revolutions, deliver short-term disruptions followed by long-term benefits, or will it be the catalyst for new ways of learning and upskilling and help reduce the widening digital divide?
AI researchers have built a Minecraft bot that can explore and expand its capabilities in the game’s open world — but unlike other bots, this one basically wrote its own code through trial and error and lots of GPT-4 queries.
Called Voyager, this experimental system is an example of an “embodied agent,” an AI that can move and act freely and purposefully in a simulated or real environment. Personal assistant type AIs and chatbots don’t have to actually do stuff, let alone navigate a complex world to get that stuff done. But that’s exactly what a household robot might be expected to do in the future, so there’s lots of research into how they might do that.
Minecraft is a good place to test such things because it’s a very (very) approximate representation of the real world, with simple and straightforward rules and physics, but it’s also complex and open enough that there’s lots to accomplish or try. Purpose-built simulators are great, too, but they have their own limitations.
As AI continues to develop at a rapid rate, concerns have been raised by experts and the so-called AI ‘godfathers’ about the imminent risks it could pose to people’s privacy, human rights, and safety.
Amid steps taken by the United Kingdom government and the European Union, in partnership with the U.S, to regulate the technology, a member of the non-statutory AI Council of the U.K. government has said that the very powerful artificial general intelligence (AGI) systems may eventually have to be banned.
Midcap information technology (IT) services firm, Mphasis, on Thursday, announced the setting up of a business unit dedicated to generative artificial intelligence (AI). The unit will offer advisory on adoption of generative AI solutions to clients, develop the company’s own generative AI properties, offer licenses to over 250 AI models through the company’s ‘Hyperscaler’ solutions marketplace for clients, partnerships with 50 startups for helping clients build solutions, and offer conversational AI tools such as chatbots for clients to deploy in their business.
Anup Nair, who has so far served as senior vice-president and chief technology officer (CTO) of Mphasis Digital, will helm the unit, called Mphasis.ai, as its chief architect and CTO.
The launch of the dedicated business unit comes after a flurry of similar launches by pretty much every large-cap IT services firm in the country. On 6 April, Tech Mahindra became the first of the large domestic IT firms to launch a generative AI solution, called ‘Generative AI Studio’, to help clients deploy the technology for content generation use cases.
Joscha Bach is a cognitive scientist focusing on cognitive architectures, consciousness, models of mental representation, emotion, motivation and sociality.
0:00:00 Introduction. 0:00:17 Bach’s work ethic / daily routine. 0:01:35 What is your definition of truth? 0:04:41 Nature’s substratum is a “quantum graph”? 0:06:25 Mathematics as the descriptor of all language. 0:13:52 Why is constructivist mathematics “real”? What’s the definition of “real”? 0:17:06 What does it mean to “exist”? Does “pi” exist? 0:20:14 The mystery of something vs. nothing. Existence is the default. 0:21:11 Bach’s model vs. the multiverse. 0:26:51 Is the universe deterministic. 0:28:23 What determines the initial conditions, as well as the rules? 0:30:55 What is time? Is time fundamental? 0:34:21 What’s the optimal algorithm for finding truth? 0:40:40 Are the fundamental laws of physics ultimately “simple”? 0:50:17 The relationship between art and the artist’s cost function. 0:54:02 Ideas are stories, being directed by intuitions. 0:58:00 Society has a minimal role in training your intuitions. 0:59:24 Why does art benefit from a repressive government? 1:04:01 A market case for civil rights. 1:06:40 Fascism vs communism. 1:10:50 Bach’s “control / attention / reflective recall” model. 1:13:32 What’s more fundamental… Consciousness or attention? 1:16:02 The Chinese Room Experiment. 1:25:22 Is understanding predicated on consciousness? 1:26:22 Integrated Information Theory of consciousness (IIT) 1:30:15 Donald Hoffman’s theory of consciousness. 1:32:40 Douglas Hofstadter’s “strange loop” theory of consciousness. 1:34:10 Holonomic Brain theory of consciousness. 1:34:42 Daniel Dennett’s theory of consciousness. 1:36:57 Sensorimotor theory of consciousness (embodied cognition) 1:44:39 What is intelligence? 1:45:08 Intelligence vs. consciousness. 1:46:36 Where does Free Will come into play, in Bach’s model? 1:48:46 The opposite of free will can lead to, or feel like, addiction. 1:51:48 Changing your identity to effectively live forever. 1:59:13 Depersonalization disorder as a result of conceiving of your “self” as illusory. 2:02:25 Dealing with a fear of loss of control. 2:05:00 What about heart and conscience? 2:07:28 How to test / falsify Bach’s model of consciousness. 2:13:46 How has Bach’s model changed in the past few years? 2:14:41 Why Bach doesn’t practice Lucid Dreaming anymore. 2:15:33 Dreams and GAN’s (a machine learning framework) 2:18:08 If dreams are for helping us learn, why don’t we consciously remember our dreams. 2:19:58 Are dreams “real”? Is all of reality a dream? 2:20:39 How do you practically change your experience to be most positive / helpful? 2:23:56 What’s more important than survival? What’s worth dying for? 2:28:27 Bach’s identity. 2:29:44 Is there anything objectively wrong with hating humanity? 2:30:31 Practical Platonism. 2:33:00 What “God” is. 2:36:24 Gods are as real as you, Bach claims. 2:37:44 What “prayer” is, and why it works. 2:41:06 Our society has lost its future and thus our culture. 2:43:24 What does Bach disagree with Jordan Peterson about? 2:47:16 The millennials are the first generation that’s authoritarian since WW2 2:48:31 Bach’s views on the “social justice” movement. 2:51:29 Universal Basic Income as an answer to social inequality, or General Artificial Intelligence? 2:57:39 Nested hierarchy of “I“s (the conflicts within ourselves) 2:59:22 In the USA, innovation is “cheating” (for the most part) 3:02:27 Activists are usually operating on false information. 3:03:04 Bach’s Marxist roots and lessons to his former self. 3:08:45 BONUS BIT: On societies problems.
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I’m producing an imminent documentary Better Left Unsaid http://betterleftunsaidfilm.com on the topic of “when does the left go too far?” Visit that site if you’d like to contribute to getting the film distributed (in 2020) and seeing more conversations like this.
Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia. According to the BBC, a paper in Nature Chemical Biology describes how the researchers used training data that measured known drugs’ action on the tough bacteria. The learning algorithm then projected the effect of 6,680 compounds with no data on their effectiveness against the germ.
In an hour and a half, the program reduced the list to 240 promising candidates. Testing in the lab found that nine of these were effective and that one, now called abaucin, was extremely potent. While doing lab tests on 240 compounds sounds like a lot of work, it is better than testing nearly 6,700.
Interestingly, the new antibiotic seems only to be effective against the target microbe, which is a plus. It isn’t available for people yet and may not be for some time — drug testing being what it is. However, this is still a great example of how machine learning can augment human brainpower, letting scientists and others focus on what’s really important.
Vice President of AI & Quantum Computing, Paul Smith-Goodson gives his analysis of quantum machine learning models and IonQ’s strategy to make it a reality.
