Archive for the ‘Quantum Computer’ Category

For Thomas Searles, a passion for people and science at HBCUs and MIT – MIT News

When Thomas Searles was assigned a book report in the first grade, he initially had trouble choosing a topic. He really didnt like fiction books. After a bit of indecision, he chose to write his report on a book about Black astronauts. Though he didnt realize it at the time, his journey to becoming a physicist at MIT had just begun.

I looked in the book, and there was Ronald E. McNair, who happens to be an MIT alum, randomly; he got his PhD here, Searles says. And it said that he was a laser physicist. So, I said, Well, that's what I'm going to be, because I want to be an astronaut.

Searles is now a member of the 2020-21 Martin Luther King (MLK) Visiting Professors and Scholars Program cohort at MIT. Since 1995, the MLK Scholars Program has brought in a total of 67 visiting professors and 21 visiting scholars from across all academic disciplines. Individuals of any underrepresented minority group are eligible to apply, and scholars are selected for their contributions both to their fields and their potential contributions to MIT.

It's something that was always on my radar as a young Black scientist, Searles said. It was something that was on my five- to 10-year plan.

Searles is currently an associate professor in the Department of Physics at Howard University, a historically Black college and university (HBCU) located in Washington. There, he established a new research program in applied and materials physics. He is also the director of a new academic partnership between IBM and 13 other HBCUs called the IBM-HBCU Quantum Center.

Searles research career began as an undergraduate in mathematics and physics at Morehouse College, a HBCU in Atlanta. Before graduating in 2005, he worked in an optics lab, examining the properties of light and its interactions with matter.

A lot of us had an interest in optics, because that was the only experimental lab that we had at Morehouse at the time, Searles says. So naturally, I applied to graduate schools that were optics-related.

That interest led him to pursue his PhD in applied physics in the Department of Electrical and Computer Engineering at Rice University in Houston, Texas, from which he graduated. Before graduating in 2011, he studied light-matter interactions, and completed a thesis about the magneto-optical properties of carbon nanotubes, tiny cylinders comprised of a single layer of carbon atoms. Carbon nanotubes are extremely strong, lightweight, and electrically conductive, making them promising for a variety of applications.

In 2015, Searles started at Howard University. I wanted to go back and work at an HBCU. I thought of my experience working in the Morehouse optics lab and how they kind of shaped my experience, Searles says. So then I was like, What can I do that's different from everyone else that will also provide opportunities to a lot of Black students? So, I set out to start a terahertz experimental lab, knowing that it was going to be difficult. And it was difficult. But we were able to do it.

In the terahertz spectroscopy lab at Howard University, researchers work with matter that has a large wavelength, and a frequency between several hundred gigahertz and several terahertz. During the first so-called quantum age in the mid-1900s, silicon was the new, exciting material used to develop transistors. Now, researchers in fields like chemistry and physics are on the hunt for the next material to be a platform for a new generation of quantum technologies.

The primary goal is to study materials for new computers, making them either safer, faster, or more secure, Searles says. This whole idea of quantum computing is what we're focusing our lab on, moving towards this idea of quantum advantage.

Quantum computing relies upon the use of quantum materials which have unique electronic and magnetic properties to build faster, stronger, and more powerful computers. Such machines are likely to provide this quantum advantage for new developments in medicine, science, finance, chemistry, and many other fields.

In 2016, Searles met MIT associate professor of physics and Mitsui Career Development Professor in Contemporary Technology Joseph Checkelsky at an event through the National Science Foundation Center for Integrated Quantum Materials.

The idea was to try to find people that we wanted to collaborate and work with, Checkelsky says. And I think I even wrote down in my notepad Thomas' name and put a big underline that I should work with this guy. Searles says the best thing that can ever happen to a spectroscopist like himself is to find a crystal-growth person that provides samples, who you also really vibe with and like as a person. And that person for me has been Joe. The two have been collaborating ever since.

Checkelskys lab works to discover new crystalline materials that enable quantum phenomena. For instance, one material that has previously been of interest to Checkelsky is a kagome crystal lattice, a 2D arrangement of iron and tin molecules. Both Checkelsky and Searles are interested in applying a branch of mathematics called topology to solids, particularly semimetals.

One of the roles Thomas plays is to examine the optical properties of these new systems to understand how light interacts with quantum materials, Checkelsky says. Its not only fundamentally important, it can also be the bridge that connects to new technologies that interfaces light with quantum science.

Searles expertise on the optics side of the research enables him to identify which materials are ideal for further study, while Checkelskys group is able to synthesize materials with certain properties of interest.

It's a cycle of innovation where his lab knows how it can be tested and my lab knows how to generate the material, Checkelsky says. Each time we get through the cycle is another step toward answering questions in fundamental science that can also bring us to new platforms for quantum technology.

Checkelsky nominated Searles for the MLK Scholars Program in hopes of further expanding their academic partnership. He now serves as Searles host researcher through the program.

I hope to extend my collaboration with Joe to not only [explore] this condensed matter, experimental side of my group, but to expand this into Lincoln Laboratory and the quantum information portion that MIT has, Searles says. I think that's critical, research-wise.

In addition to their research goals, Searles and Checkelsky are excited to strengthen the general connection between MIT and Howard.

I think there are opportunities for Thomas to see, for example, the graduate school process in our department, Checkelsky says. Along the same lines, it is a great opportunity for MIT and our department to learn more how to connect to the people and science within HBCUs. It is a great chance for information to flow both ways.

Searles also hopes to encourage more HBCU students to pursue graduate study at MIT. The goal of increasing the number of qualified applicants [from HBCUs] I think that's something that I can measure metrically from the first year, Searles says. And if there's anything that I can do to help with that number, I think that would be awesome.

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For Thomas Searles, a passion for people and science at HBCUs and MIT - MIT News

Outlook on the AI Chipsets Global Market to 2025 – More than 83% of Global Chipsets Will be AI-equipped – PRNewswire

DUBLIN, Nov. 6, 2020 /PRNewswire/ -- The "AI Chipsets for Wireless Networks and Devices, Cloud and Next Generation Computing, IoT, and Big Data Analytics 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This report evaluates leading market players across the AI chipsets ecosystem, technology strategies, and solution plans. This includes leveraging AI chipsets for support of various emerging and disintermediating technology areas such as edge computing, 5G, and blockchain systems. Additional areas addressed include AI support of emerging computing technologies including edge platforms and servers.

This report also assesses applications and service support scenarios for AI chipsets across almost all major industry verticals. The report provides forecasts for AI chipset hardware, embedded software, professional service, deployment platforms, and applications for every major industry vertical as well as regional and country forecasts for 2020 to 2025. The report also provides exclusive recommendations for stakeholders within the AI chipsets ecosystem.

The AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. With 83% of all chipsets globally shipping AI-equipped, over 57% of all electronics will have some form of embedded intelligence by 2025. This will also be transformational for existing critical business functions such as identity management, authentication, and cybersecurity.

Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden patterns among data, predict actionable insights and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance. This will be a boon for many aspects of ICT ranging from decision support and data analytics to product safety and system optimization.

Consumers will realize benefits indirectly through improved product and service performance such as device and cloud-based gaming. Enterprise and industrial users will benefit through general improvements in automated decision-making, especially in the areas of robotic process automation, decision support systems, and overall data management. AI chipsets will be particularly useful for business edge equipment for real-time data analytics and store versus processing decisions.

Select Report Findings:

Key Topics Covered:

1. Executive Summary

2. Research Overview2.1 Research Objectives2.2 Select Findings

3. AI Chipsets Introduction3.1 AI Chipsets3.1.1 Chipset Components3.1.2 General Purpose Applications3.2 AI Systems3.3 Market Dynamics Analysis3.4 AI Investments3.5 Competitive Market

4. Technologies, Solutions, and Markets4.1 Chipsets Technology and Products4.2 AI Technology4.2.1 Machine Learning4.2.2 Machine Learning APIs4.2.3 Deep Machine Learning4.2.4 Natural Language Processing4.2.5 Computer Vision4.2.6 Voice Recognition4.2.7 Context Awareness Computing4.2.8 Neural Networks4.2.9 Facial Recognition4.3 Deployment Platform4.4 IoT Sector4.5 Applications in Industry Verticals4.6 Regional Markets4.7 Value Chain4.8 5G Network and Edge Computing4.9 Cloud Computing and Data Analytics4.10 Industry 4.0 and Factory Automation4.11 Autonomous Networks4.12 Blockchain Networks4.13 Quantum Computing4.14 Machine Intelligence4.15 Nanoscale Technology4.16 Mobile Network Operators

5. Company Analysis5.1 NVIDIA Corporation5.2 IBM Corporation5.3 Intel Corporation5.4 Samsung Electronics Co Ltd.5.5 Microsoft Corporation5.6 Baidu Inc.5.7 Qualcomm Incorporated5.8 Huawei Technologies Co. Ltd.5.9 Fujitsu Ltd.5.10 Softbank Group Corp. (ARM Limited)5.11 Apple Inc.5.12 Amazon Inc. (AWS)5.13 SK Telecom5.14 Inbenta Technologies Inc.5.15 Microchip Technology Inc.5.16 Texas Instruments Inc.5.17 Advanced Micro Devices (AMD) Inc.5.18 XILINX Inc.5.19 Micron Technology5.20 AIBrain Inc.5.21 General Vision Inc.5.22 Sentient Technologies Holdings Limited5.23 Graphcore5.24 Analog Devices Inc.5.25 Cypress Semiconductor Corp5.26 Rohm Semiconductor5.27 Semtech Corporation5.28 NXP Semiconductors N.V.5.29 STMicroelectronics5.30 MediaTek Inc.5.31 Renesas Electronics Corporation5.32 ZTE Corporation5.33 NEC Corporation5.34 Broadcom Corporation5.35 Integrated Device Technology (IDT) Inc.5.36 Toshiba Corporation5.37 Adapteva Inc.5.38 Applied Materials Inc.5.39 Bitmain Technologies Inc.5.40 Cambricon Technologies Corporation Limited5.41 DeePhi Tech5.42 Gyrfalcon Technology Inc.5.43 Horizon Robotics5.44 Mythic5.45 Tenstorrent Inc.5.46 Wave Computing5.47 Mellanox Technologies5.48 Koniku5.49 Numenta Inc.5.50 Imagination Technologies Limited5.51 Synopsys Inc.5.52 SenseTime5.53 Marvell Technology Group Ltd.5.54 Cadence Design Systems Inc.5.55 Rockchip5.56 VeriSilicon Limited5.57 Knuedge Inc.5.58 KRTKL Inc.5.59 Shanghai Think-Force Electronic Technology Co. Ltd.5.60 SK Hynix Inc.5.61 Taiwan Semiconductor Manufacturing Company Limited (TSMC)5.62 Alphabet (Google)5.63 Thinci5.64 LG Corporation5.65 SambaNova Systems5.66 Groq5.67 Kalray5.68 Facebook5.69 Almotive5.70 AnotherBrain5.71 BrainChip Holdings5.72 Cerebras Systems5.73 Chipintelli5.74 Tesla (DeepScale)5.75 Kneron5.76 NovuMind5.77 ThinkForce5.78 Vathys5.79 Nervana Systems5.80 Barefoot Networks5.81 Alibaba Group5.82 Megvii5.83 HPE5.84 Dell Inc. (Dell EMC)5.85 Western Digital5.86 Habana5.87 Nokia

6. AI Chipsets Market Analysis and Forecasts 2020 - 20256.1 Global AI Chipsets Market 2020 - 20256.1.1 Total Global Market Size 2020 - 20256.1.2 Market by Segment 2020 - 20256.1.3 Market by Deployment Platform 2020 - 20256.1.4 Market by Application 2020 - 20256.1.5 Market by Industry Vertical 2020 - 20256.1.6 Market by AI in Consumer, Enterprise, Industrial and Government 2020 - 20256.1.7 Market in 5G Networks 2020 - 20256.1.8 Market in Edge Computing Networks 2020 - 20256.1.9 Market in Cloud Computing 2020 - 20256.1.10 Market in Quantum Computing 2020 - 20256.1.11 Market in Big Data Analytics 2020 - 20256.1.12 Market in IoT 2020 - 20256.1.13 Market in Blockchain Networks 2020 - 20256.2 Regional AI Chipsets Market 2020 - 20256.2.1 Market by Region 2020 - 20256.2.1.1 North America Market 2020 - 20256.2.1.2 Europe Market 2020 - 20256.2.1.3 Asia Pacific Market 2020 - 20256.2.1.4 Middle East and Africa Market 2020 - 20256.2.1.5 Latin America Market 2020 - 20256.3 AI Chipsets Deployment Forecast 2020 - 20256.3.1 Total Global Deployment 2020 - 20256.3.2 Deployment by Segment 2020 - 20256.3.2.1 Deployment by Product 2020 - 20256.3.2.2 Deployment by Technology 2020 - 20256.3.2.3 Deployment by Processor Type 2020 - 20256.3.3 Deployment by Platform 2020 - 20256.3.3.1 Deployment by IoT Device 2020 - 20256.3.3.1.1 Deployment by Wearable Device 2020 - 20256.3.3.1.2 Deployment by Healthcare Device 2020 - 20256.3.3.1.3 Deployment by Smart Appliances 2020 - 20256.3.3.1.4 Deployment by Industrial Machines 2020 - 20256.3.3.1.5 Deployment by Entertainment Device 2020 - 20256.3.3.1.6 Deployment by Security Device 2020 - 20256.3.3.1.7 Deployment by Network Device 2020 - 20256.3.3.1.8 Deployment by Connected Vehicle Device 2020 - 20256.3.3.1.9 Deployment by Smart Grid Device 2020 - 20256.3.3.1.10 Deployment by Military Device 2020 - 20256.3.3.1.11 Deployment by Energy Management Device 2020 - 20256.3.3.1.12 Deployment by Agriculture Specific Device 2020 - 20256.3.3.2 Deployment by Non-IoT Device 2020 - 20256.3.3.3 Deployment by IoT "Things" 2020 - 20256.3.4 Deployment by AI Technology 2020 - 20256.3.4.1 Deployment by AI Technology Type 2020 - 20256.3.4.2 Deployment by Machine Learning Technology 2020 - 20256.3.5 Deployment by Application 2020 - 20256.3.6 Deployment by Industry Vertical 2020 - 20256.3.7 Deployment by Price Range 2020 - 20256.3.8 Deployment by Region 2020 - 20256.3.8.1 North America Deployment by Country 2020 - 20256.3.8.2 Europe Deployment by Country 2020 - 20256.3.8.3 Asia Pacific Deployment by Country 2020 - 20256.3.8.4 Middle East and Africa Deployment by Country 2020 - 20256.3.8.5 Latin America Deployment by Country 2020 - 2025

7. Conclusions and Recommendations7.1 Advertisers and Media Companies7.2 Artificial Intelligence Providers7.3 Automotive Companies7.4 Broadband Infrastructure Providers7.5 Communication Service Providers7.6 Computing Companies7.7 Data Analytics Providers7.8 Immersive Technology (AR, VR, and MR) Providers7.9 Networking Equipment Providers7.10 Networking Security Providers7.11 Semiconductor Companies7.12 IoT Suppliers and Service Providers7.13 Software Providers7.14 Smart City System Integrators7.15 Automation System Providers7.16 Social Media Companies7.17 Workplace Solution Providers7.18 Large Businesses, SMBs, and Governments7.19 Future Market Direction

Companies Mentioned

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Outlook on the AI Chipsets Global Market to 2025 - More than 83% of Global Chipsets Will be AI-equipped - PRNewswire

Want to visit 2 million stars? Theres a shortcut, but youll need a warp drive – SYFY WIRE

Its towel day. Really. Someone has actually come up with a real hitchhikers guide to the galaxy, though there are no directions to the Restaurant at the End of the Universe.

The shortest path to take in order to visit 2 million stars has been found by mathematicians William Cook and Keld Helsgaun. They have finally solved a long-standing mystery. By analyzing data from the Gaia space telescope thatincluded 2,079,471 stars in our galaxy, they mapped the most efficient 3D route to hop over to each of them once, and the margin for error is extremely small. The only problem is that youd need a warp drive that could travel at the speed of light (at least), and were definitely not there yet. That, and you would probably have to be immortal or close to it, as the journey would still take about 100 million years.

Cook and Helsgaun were after the solution to the traveling salesman problem, which asks how you could take the shortest route between multiple destinations with only one stop at each. The journey would eventually bring you back to where you started from, which, in this scenario, is the sun. Helsgaun searched for ways to find better and better tours, while Cook was the one who proved guarantees on how short a tour could possibly be. The tours inform the guarantees while the guarantees help improve the tours. Figuring out how to do this with the stars in our galaxy puts it on the largest scale ever.

To make the rotating maps of the tours, we used the three.js Javascript 3D library, Cook told SYFY WIRE. The 3D positions of the stars were taken from a data set created at the Max Planck Institute for Astronomy. We used somewhere between 100 and 200 CPU years in the computations, running on a network of computers when they were not otherwise occupied.

The problem took up to 200 years of computing time that was compressed into just two years (it is faster when you dont have thousands of people trying to log onto the same network). Quantum computers could potentially speed up that process, but Cook has doubts. If someone could come up with technology advanced enough for a huge and extremely strong quantum computer, it could help with finding shorter tours, and quantum computing search could further help in shortening those routes. The problem is that were not technologically there yet. The quantum computers that do exist are just unable to support such an extreme dataset, let alone dream up every possible tour at once.

It is not at all clear that a quantum computer can help in solving large instances of the traveling salesman problem, Cook said. In particular, there is no indication that quantum computing can help substantially in finding a guarantee needed to prove a tour is shortest possible.

Gaia, whose mission is to make the largest and most precise 3D map of the Milky Way, has released data on the locations of 1.33 billion stars, so Cook and Helsgaun are now trying to figure out the shortest route between them. This is a dataset 500 times larger than the last. Their best result yet is over 15 trillion light years. So far, they can only guarantee that it is at most about a factor of 1.0038 longer than the shortest possible tour, which seems like nothing, but is a far greater margin for error than the factor of 0.0000074, which is 700 light years for that particular route. Not bad compared to the nearly hundred thousand the entire trip would take. Even then, Cook still wants to push it further.

We have found a set of rules (using parallel computing) that we hope will give a strong guarantee, but the hugescale of the problem makes it difficult to find thecombination of the rules that we need, he said. Thiscombinationtask is calledlinear programming it is the workhorse for the field of mathematical optimization. Our 1.33-billon star project is driving thecreation of LPalgorithms to handle examples nearly 1,000 times larger than waspreviously possible.

By the way, because Cook and Helsgaun believe that the 2 million-star tour could be done in even less time than the guarantee, they are offering a reward* of $50 for each parsec (3.26 light years) that can be saved by rearranging the route to those 2,079,471 stars, up to a $10,000 total. Just saying.

*It's legit. Cook personally asked your friendly neighborhood writer to spread the word about this.

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Want to visit 2 million stars? Theres a shortcut, but youll need a warp drive - SYFY WIRE

House Democrats introduce bill to invest $900 billion in STEM research and education | TheHill – The Hill

Rep. Ro KhannaRohit (Ro) KhannaHouse Democrats introduce bill to invest 0 billion in STEM research and education Biden says he opposes Supreme Court term limits Dozens of legal experts throw weight behind Supreme Court term limit bill MORE (D-Calif.) and several other House Democrats introduced legislation on Tuesday to invest in and train a technologically proficient workforce for the future.

The 21st Century Jobs Act would invest $900 billion over ten years in research and development efforts around emerging technologies including artificial intelligence (AI), cybersecurity and biotechnology, along with prioritizing science, technology, engineering and mathematics (STEM) education.

It would establish a Federal Institute of Technology (FIT) that would be spread out across the nation at 30 different locations including existing educational facilities, along with promoting STEM education in public schools.

Specifically, the bill would help fund computer science courses for K-12 students, carve out scholarships for those pursuing degrees in the STEM fields, allocate $8 billion to train teachers in STEM fields, and create tax incentives for companies to hire individuals who attended a FIT institution orreceived a STEM scholarship in order to diversify the talent field.

According to a summary of the bill, it would ultimately create around3 million new jobs per year, and significantly raise public investment in research and development, helping the U.S. keep pace with other nations on the international stage.

The bill is also sponsored by DemocraticReps. Nanette Barragn (Calif.), Suzan DelBeneSuzan Kay DelBeneHouse Democrats introduce bill to invest 0 billion in STEM research and education Democrats sense momentum for expanding child tax credit Democrats say affordable housing would be a top priority in a Biden administration MORE (Wash.), Dwight EvansDwight (Dewey) EvansHouse Democrats introduce bill to invest 0 billion in STEM research and education Will the next coronavirus relief package leave essential workers behind? Bipartisan GROCER Act would give tax break to frontline workers MORE (Penn.), Jim HimesJames (Jim) Andres HimesHouse Democrats introduce bill to invest 0 billion in STEM research and education Overnight Defense: Pentagon IG to audit use of COVID-19 funds on contractors | Dems optimistic on blocking Trump's Germany withdrawal | Obama slams Trump on foreign policy House panel urges intelligence community to step up science and technology efforts MORE (Conn.), Pramila JayapalPramila JayapalHouse Democrats introduce bill to invest 0 billion in STEM research and education Ocasio-Cortez, progressives call on Senate not to confirm lobbyists or executives to future administration posts Pocan won't seek another term as Progressive Caucus co-chair MORE (Wash.) Tim RyanTimothy (Tim) RyanHouse Democrats introduce bill to invest 0 billion in STEM research and education Now's the time to make 'Social Emotional Learning' a national priority Mourners gather outside Supreme Court after passing of Ruth Bader Ginsburg MORE (Ohio) and Darren SotoDarren Michael SotoHouse Democrats introduce bill to invest 0 billion in STEM research and education Radiation elevated at fracking sites, researchers find Hopes for DC, Puerto Rico statehood rise MORE (Fla.), as well as House Homeland Security Committee Chairman Bennie ThompsonBennie Gordon ThompsonHouse Democrats introduce bill to invest 0 billion in STEM research and education Long-shot Espy campaign sees national boost in weeks before election House chairman asks Secret Service for briefing on COVID-19 safeguards for agents MORE (D-Miss.).

Several former Democratic tech-related officials endorsed the bill on Tuesday, including former Vice President Joe BidenJoe BidenGiuliani goes off on Fox Business host after she compares him to Christopher Steele Trump looks to shore up support in Nebraska Jeff Daniels narrates new Biden campaign ad for Michigan MOREs former chief economist Jared Bernstein, who said in a statement that weve got tremendous international catch-up to do in this space, and this proposal is the first Ive seen thats scaled to the magnitude of the challenge.

Ro Khannas 21st Century Jobs Package is advancing an important, ambitious agenda that would both increase economic growth and also help more people benefit from that growth, Jason FurmanJason FurmanHouse Democrats introduce bill to invest 0 billion in STEM research and education On The Money: Five things to know about the August jobs report Dates and developments to watch as we enter the home stretch MORE, a professor of the Practice of Economic Policy at Harvard University and the former chair of the Council of Economic Advisers during the Obama administration, said in a separate statement.

Khannas proposal would unleash the largest race to the top in American history as areas around the country compete not to provide tax benefits for private companies but instead to improve education, infrastructure, housing, and the climate for local innovation and development, Furman added.

Investment in developing technologies and in STEM education and workforce has been a rare topic of bipartisan support on Capitol Hill. Sens. Jacky RosenJacklyn (Jacky) Sheryl RosenHouse Democrats introduce bill to invest 0 billion in STEM research and education Hillicon Valley: Productivity, fatigue, cybersecurity emerge as top concerns amid pandemic | Facebook critics launch alternative oversight board | Google to temporarily bar election ads after polls close Lawmakers introduce legislation to boost cybersecurity of local governments, small businesses MORE (D-Nev.) and Cindy Hyde-Smith (R-Miss.)introduced legislation in September to provide $50 million to help small and medium-sized businesses hire and train professionals in the STEM field, particularly those who are female, Black or Latino or from rural areas.

A bipartisan group of senators led by Senate Minority Leader Chuck SchumerChuck SchumerHouse Democrats introduce bill to invest 0 billion in STEM research and education Graham dismisses criticism from Fox Business's Lou Dobbs Lewandowski: Trump 'wants to see every Republican reelected regardless of ... if they break with the president' MORE (D-N.Y.) introduced a separate bill in May that would funnel $100 billion over five years into U.S. science and technology research.

The Trump administration has also zeroed in on promoting investment in emerging science and technology fields.

The U.S. and the United Kingdom signed a formal agreement last month to promote cooperation on AI development, while the administration announced in August it would funnel over $1 billion over the next five years into funding new research institutes focused on AI and quantum computing development.

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House Democrats introduce bill to invest $900 billion in STEM research and education | TheHill - The Hill

Why AI Geniuses Haven’t Created True Thinking Machines – Walter Bradley Center for Natural and Artificial Intelligence

As we saw yesterday, artificial intelligence (AI) has enjoyed a a string of unbroken successes against humans. But these are successes in games where the map is the territory. Therefore, everything is computable.

That fact hints at the problem tech philosopher and futurist George Gilder raises in Gaming AI (free download here). Whether all human activities can be treated that way successfully is an entirely different question. As Gilder puts it, AI is a system built on the foundations of computer logic, and when Silicon Valleys AI theorists push the logic of their case to a singularity, they defy the most crucial findings of twentieth-century mathematics and computer science.

Here is one of the crucial findings they defy (or ignore): Philosopher Charles Sanders Peirce (18391914) pointed out that, generally, mental activity comes in threes, not twos (so he called it triadic). For example, you see a row of eggs in a carton and think 12. You connect the objects (eggs) with a symbol, 12.

In Peirces terms, you are the interpretant, the one for whom the symbol 12 means something. But eggs are not 12. 12 is not eggs. Your interpretation is the third factor that makes 12 mean something with respect to the eggs.

Gilder reminds us that, in such a case, the map is not the territory (p. 37) Just as 12 is not the eggs, a map of California is not California. To mean anything at all, the map must be read by an interpreter. AI supremacy assumes that the machines map can somehow be big enough to stand in for the reality of California and eliminate the need for an interpreter.

The problem, he says, is that the map is not and never can be reality. There is always a gap:

Denying the interpretant does not remove the gap. It remains intractably present. If the inexorable uncertainty, complexity, and information overflows of the gap are not consciously recognized and transcended, the gap fills up with noise. Congesting the gap are surreptitious assumptions, ideology, bias, manipulation, and static. AI triumphalism allows it to sink into a chaos of constantly changing but insidiously tacit interpretations.

Ultimately AI assumes a single interpretant created by machine learning as it processes ever more zettabytes of data and converges on a single interpretation. This interpretation is always of a rearview mirror. Artificial intelligence is based on an unfathomably complex and voluminous look at the past. But this look is always a compound of slightly wrong measurements, thus multiplying its errors through the cosmos. In the real world, by contrast, where interpretation is decentralized among many individual mindseach person interpreting each symbolmistakes are limited, subject to ongoing checks and balances, rather than being inexorably perpetuated onward.

Does this limitation make a difference in practice? It helps account for the ongoing failure of Big Data to provide consistently meaningful correlations in science, medicine, or economics research. Economics professor Gary Smith puts the problem this way:

Humans naturally assume that all patterns are significant. But AI cannot grasp the meaning of any pattern, significant or not. Thus, from massive number crunches, we may learn (if thats the right word) that

Stock prices can be predicted from Google searches for the word debt.

Stock prices can be predicted from the number of Twitter tweets that use calm words.

An unborn babys sex can be predicted by the amount of breakfast cereal the mother eats.

Bitcoin prices can be predicted from stock returns in the paperboard-containers-and-boxes industry.

Interest rates can be predicted from Trump tweets containing the words billion and great.

If the significance of those patterns makes no sense to you, its not because you are not as smart as the Big Data machine. Those patterns shouldnt make any sense to you. Theres no sense in them because they are meaningless.

Smith, author with Jay Cordes of The Phantom Pattern Problem (Oxford, 2020), explains that these phantom patterns are a natural occurrence within the huge amounts of data that big computers crunch:

even random data contain patterns. Thus the patterns that AI algorithms discover may well be meaningless. Our seduction by patterns underlies the publication of nonsense in good peer-reviewed journals.

Yes, such meaningless findings from Big Data do creep into science and medicine journals. Thats partly a function of thinking that a big computer can do our thinking for us even though it cant recognize the meaning of patterns. Its what happens when there is no interpreter.

Ah, butso we are toldquantum computers will evolve so as to save the dream of true thinking machines. Gilder has thought about that one too. In fact, hes been thinking about it since 1989 when he published Microcosm: The Quantum Era in Economics and Technology.

Its true that, in the unimaginably tiny quantum world, electrons can do things we cant:

A long-ago thought experiment of Einsteins showed that once any two photonsor other quantum entitiesinteract, they remain in each others influence no matter how far they travel across the universe (as long as they do not interact with something else). Schrdinger christened this entanglement: The spinor other quantum attributeof one behaves as if it reacts to what happens to the other, even when the two are impossibly remote.

But, he says, its also true that continuously observing a quantum system will immobilize it (the quantum Zeno effect). As John Wheeler reminded us, we live in a participatory universe where the observer (Peirces interpretant) is critical. So quantum computers, however cool they sound, still play by rules where the interpreter matters.

In any event, at the quantum scale, we are trying to measure atoms and electrons using instruments composed of atoms and electrons (p. 41). That is self-referential and introduces uncertainty into everything: With quantum computing, you still face the problem of creating an analog machine that does not accumulate errors as it processes its data (p. 42). Now we are back where we started: Making the picture within the machine much bigger and more detailed will not make it identical to the reality it is supposed to interpret correctly.

And remember, we still have no idea how to make the Ultimate Smart Machine conscious because we dont know what consciousness is. We do know one thing for sure now: If Peirce is right, we could turn most of the known universe into processors and still not produce an interpreter (the consciousness that understands meaning).

Robert J. Marks points out that human creativity is non-algorithmic and therefore uncomputable. From which Gilder concludes, The test of the new global ganglia of computers and cables, worldwide webs of glass and light and air, is how readily they take advantage of unexpected contributions from free human minds in all their creativity and diversity. These high-entropy phenomena cannot even be readily measured by the metrics of computer science (p. 46).

Its not clear to Gilder that the AI geniuses of Silicon Valley are taking this in. The next Big Fix is always just around the corner and the Big Hype is always at hand.

Meanwhile, the rest of us can ponder an idea from technology philosopher George Dyson, Complex networksof molecules, people or ideasconstitute their own simplest behavioral descriptions. (p. 53) He was explaining why analog quantum computers would work better than digital ones. But, considered carefully, his idea also means that you are ultimately the best definition of you. And thats not something that a Big Fix can just get around.

Heres the earlier article: Why AI geniuses think they can create true thinking machines. Early on, it seemed like a string of unbroken successes In Gaming AI, George Gilder recounts the dizzying achievements that stoked the ambitionand the hidden fatal flaw.

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Why AI Geniuses Haven't Created True Thinking Machines - Walter Bradley Center for Natural and Artificial Intelligence