Lifeboat Foundation

A newly created nano-architected material exhibits a property that previously was just theoretically possible: it can refract light backward, regardless of the angle at which the light strikes the material.

Researchers from KTH Royal Institute of Technology and Stanford University have fabricated a material for computer components that enables the commercial viability of computers that mimic the human brain.

Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward commercialization of powerful computers that are modeled after the brain’s neural network. Such neuromorphic computers can be thousands times more energy efficient than today’s computers.

These advances in computing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi.

Artificial intelligence will soon become one of the most important, and likely most dangerous, aspects of the metaverse. I’m talking about agenda-driven artificial agents that look and act like any other users but are virtual simulations that will engage us in “conversational manipulation,” targeting us on behalf of paying advertisers.

This is especially dangerous when the AI algorithms have access to data about our personal interests, beliefs, habits and temperament, while also reading our facial expressions and vocal inflections. Such agents will be able to pitch us more skillfully than any salesman. And it won’t just be to sell us products and services – they could easily push political propaganda and targeted misinformation on behalf of the highest bidder.

And because these AI agents will look and sound like anyone else in the metaverse, our natural skepticism to advertising will not protect us. For these reasons, we need to regulate some aspects of the coming metaverse, especially AI-driven agents. If we don’t, promotional AI-avatars will fill our lives, sensing our emotions in real time and quickly adjusting their tactics for a level of micro-targeting never before experienced.

Second, we need to be aware of the manifest biases and fallacies that magnify the weight humans put on potential losses compared to potential future gains. As a result of these biases, humans often seek to preserve the status quo over pursuing activities that lead to future changes, even when the expected (but risky) gains from the latter may outweigh those of maintaining the status quo. The preference for the status quo, and neat narratives that oversimplify complex scenarios, can lead to overlooking (or ignoring) important information that is not consistent with the current generally accepted meme — illustrated, perhaps, in Musk’s continued optimism for autonomous vehicles despite the evidence leading to others downscaling their forecasts.

The first and second points together lead to the third important consideration: the importance of independently verified data over forecasts and opinion in determining the need for and appropriateness of policy interventions. And data is historical by nature. Pausing to collect it rather than rushing to respond is recommended.

To that end, we can use available data to analyze whether increasing use of AI is demonstrably affecting key labor market performance indicators: labor productivity and multifactor productivity growth. If, as Keynes suggests, AI-driven technological change is increasing the potential for new means of economizing the use of labor to outrun the pace of finding new ways to use it, we would expect to see both statistics rising in the era dominated by AI. Yet as Figures 1 and 2 show, the exact opposite appears true for a wide range of OECD countries. Neither does the data suggest that other key labor market indicators have changed negatively with the advent of AI. As with the computer industry, we see the effects of AI everywhere but in the productivity statistics.

What is AI, really? Jeff Dean, the head of Google’s AI efforts, explains the underlying technology that enables artificial intelligence to do all sorts of things, from understanding language to diagnosing disease — and presents a roadmap for building better, more responsible systems that have a deeper understanding of the world. (Followed by a Q&A with head of TED Chris Anderson)

Visit http://TED.com to get our entire library of TED Talks, transcripts, translations, personalized talk recommendations and more.

Continue reading “Jeff Dean: AI isn’t as smart as you think — but it could be | TED” »

64x Bio comes out of the Har­vard De­part­ment of Ge­net­ics. CEO Lex Rovn­er and her team — which right now, sits around 10 peo­ple — are look­ing to tack­le a key hur­dle for ma­jor com­pa­nies: man­u­fac­tur­ing cell and gene therapies.

Rovn­er met Church while get­ting her PhD at Yale, and went on to do a post­doc­tor­al re­search fel­low­ship in his lab, and, when talk­ing to folks in the in­dus­try, found a mas­sive vi­ral vec­tor man­u­fac­tur­ing bot­tle­neck that wasn’t be­ing talked about.

Af­ter a seed fund­ing round and the com­pa­ny’s launch in 2020, it made some noise in the in­dus­try, par­tic­u­lar­ly as Covid-19 made bot­tle­neck is­sues more vis­i­ble. There’s a wait­list to get a vec­tor from man­u­fac­tur­ers, and not much of a so­lu­tion to the problem.

Minor spoilers ahead. This video covers Matrix Resurrections, the Matrix Franchise, and their top 10 future technologies.
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SOURCES:
• https://research.aimultiple.com/artificial-general-intellige…ty-timing/
• https://www.scientificamerican.com/article/do-we-live-in-a-s…50-50/
• https://www.scientificamerican.com/article/confirmed-we-live-in-a-simulation/
• https://www.simulation-argument.com/matrix2.html.
• https://u.osu.edu/vanzandt/2018/04/18/ancestor-simulations/
• https://iep.utm.edu/solipsis/
• https://www.nickbostrom.com/superintelligence.html.
• https://in.askmen.com/tech-news/1111981/article/scientists-d…-the-brain.
• https://futurism.com/darpa-is-planning-to-hack-the-human-bra…oad-skills.
• https://www.iqsdirectory.com/resources/what-can-we-expect-fr…he-future/
• https://www.lightfieldlab.com/
• https://www.bbc.com/news/technology-53921596
• https://www.bbc.com/news/technology-49606027
• https://www.zdnet.com/article/brain-hacking-is-the-next-big-…-the-mind/
• https://futurism.com/hacking-the-human-brain-new-tech-could-make-it-a-reality.
• https://en.wikipedia.org/wiki/Superintelligence.

Continue reading “Matrix Franchise: 10 Future Technologies Becoming a Reality” »

The goal of DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program is to develop and demo new autonomy tech that enables ground combat vehicles to maneuver in unstructured, off-road terrain at speeds that are no longer limited by the autonomy software or processing time.

Columbia, Maryland — January 27, 2022. Universities Space Research Association (USRA) today announced the start of operations for phase-2 of DARPA’s Optimization with Noisy Intermediate Scale Quantum devices (ONISQ) program. This award follows the ONISQ phase 1 launch in 2020, in which USRA was selected to lead the “Scheduling Applications with Advanced Mixers” (SAAM) project, in collaboration with Rigetti Computing and, through DARPA, under DARPA-NASA Interagency agreement (IAA) 8,839 Annex 114, with the NASA Quantum AI Laboratory.