Can Red Hat give IBM another boost in earnings? We’ll find out soon – WRAL Tech Wire

RALEIGH IBM is scheduled to announced its latest quarterly earnings today after the markets close and a big question to be answered is: Will Raleigh-based Red Hat deliver another bottom-line boost even in the times of the COVID-19 crisis?

Last quarter which occurred as IBM (NYSE: IBM) was closing on the $34 billion acquistion of Red Hat the Hatters sales helped since its own sales surged 24 percent and cloud sales climbed 21 percent.

Wall Street financial firm Zacks Research notes that new CEO Arvind Krishna he took over April 6 is putting more emphasis on cloud as well as emerging tech sectors such as artificial intelligence and quantum computing. (IBM works closely with NC State on quantum research.)

IBMs new CEO spells out priorities, especially cloud, and makes exec changes as he takes over

Last quarter, IBM surprised Wall Street with Q4 revenue growth, after five straight periods of declining sales. More specifically, Red Hat revenue jumped 24%, with total cloud revenue up 21%. Despite this solid expansion from vital growth units, IBMs overall quarterly sales only climbed 0.1%, Zacks reports.

And the coronavirus could hurt sales since it emerged as a pandemic.

Our Zacks estimates call for IBMs Q1 sales to slip 1.2% from the year-ago period to $17.97 billion, Zacks says.

Meanwhile, its adjusted quarterly earnings are projected to fall by 24.4% to hit $1.70 a share. Peeking ahead, IBMs adjusted fiscal 2020 EPS figure is expected to slip 4.8%, on 2.6% lower revenue.

On top of that, the historic tech giants consensus Q1 earnings estimate has slipped nearly 13% in the last 60 days.

IBM employs thousands of people across North Carolina, including Red Hat and one of Big Blues largest corporate campuses in RTP.

Read more online.

Red Hat names longtime exec Paul Cormier as CEO, replacing Jim Whitehurst

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Can Red Hat give IBM another boost in earnings? We'll find out soon - WRAL Tech Wire

Science of Star Trek – The UCSB Current

In the most recent episode of his YouTube series Science vs. Cinema, UC Santa Barbara physicist Andy Howell takes on Star Trek: Picard, exploring how the CBS offerings presentation of supernovae and quantum computing stack up against real world science.

For Howell, the series that reviews the scientific accuracy and portrayal of scientists in Hollywoods top sci-fi films is as much an excuse to dive into exciting scientific concepts and cutting edge research.

Science fiction writers are fond of grappling with deep philosophical questions, he said. I was really excited to see that UCSB researchers were thinking about some of the same things in a more grounded way.

For the Star Trek episode, Howell spoke with series creators Alex Kurtzman and Michael Chabon, as well as a number of cast members, including Patrick Stewart. Joining him to discuss quantum science and consciousness were John Martinis a quantum expert at UC Santa Barbara and chief scientist of the Google quantum computing hardware group and fellow UCSB Physics professor Matthew Fisher. Fishers group is studying whether quantum mechanics plays a role in the brain, a topic taken up in the new Star Trek series.

Howell also talked supernovae and viticulture with friend and colleague Brian Schmidt, vice- chancellor of the Australian National University. Schmidt won the 2011 Nobel Prize in Physics for helping to discover that the expansion of the universe is accelerating.

"We started Science vs. Cinema to use movies as a jumping-off point to talk science Howell said. Star Trek Picard seemed like the perfect fit. Star Trek has a huge cultural impact and was even one of the things that made me want to study astronomy.

Previous episodes of Science vs. Cinema have separated fact from fiction in films such as Star Wars, The Current War, Ad Astra, Arrival and The Martian. The success of prior episodes enabled Howell to get early access to the show and interview the cast and crew.

"What most people think about scientific subjects probably isn't what they learned in a university class, but what they saw in a movie, Howell remarked. That makes movies an ideal springboard for introducing scientific concepts. And while I can only reach dozens of students at a time in a classroom, I can reach millions on TV or the internet.

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Science of Star Trek - The UCSB Current

Quantum Computing Market 2020 Break Down by Top Companies, Applications, Challenges, Opportunities and Forecast 2026 Cole Reports – Cole of Duty

1qb Information Technologies

Quantum Computing Market: Competitive Landscape

The last chapter of the Quantum Computing market research report focuses exclusively on the competitive landscape. It examines the main market players. In addition to a brief overview of the business, analysts provide information on their assessment and development. The list of important products in preparation is also mentioned. The competitive landscape is analyzed by understanding the companies strategies and the initiatives they have taken in recent years to overcome intense competition.

Quantum Computing Market: Drivers and Restraints

The report explains the drivers of the future of the Quantum Computing market. It assesses the different forces which should have a positive impact on the whole market. Analysts have looked at investments in research and development for products and technologies, which should give players a significant boost. In addition, the researchers undertook an analysis of the evolution of consumer behavior which should have an impact on the cycles of supply and demand in the Quantum Computing market. In this research report, changes in per capita income, improvement in the economic situation and emerging trends were examined.

The research report also explains the potential restrictions on the Quantum Computing market. The aspects assessed are likely to hamper market growth in the near future. In addition to this assessment, it offers a list of opportunities that could prove lucrative for the entire market. Analysts offer solutions to turn threats and restrictions into successful opportunities in the years to come.

Quantum Computing Market: Regional Segmentation

In the following chapters, analysts have examined the regional segments of the Quantum Computing market. This gives readers a deeper insight into the global market and allows for a closer look at the elements that could determine its evolution. Countless regional aspects, such as the effects of culture, environment and government policies, which affect regional markets are highlighted.

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Market Dynamics: The report contains important information on influencing factors, market drivers, challenges, opportunities and market trends as part of the market dynamics.

Global Market Forecast: Readers receive production and sales forecasts for the Quantum Computing market, production and consumption forecasts for regional markets, production, sales and price forecasts for the Quantum Computing market by type and consumption forecasts for the Quantum Computing market per application.

Regional Market Analysis: It can be divided into two different sections: one for the analysis of regional production and one for the analysis of regional consumption. Here, analysts share gross margin, prices, sales, production, CAGR, and other factors that indicate the growth of all regional markets examined in the report.

Market Competition: In this section, the report provides information on the situation and trends of competition, including mergers and acquisitions and expansion, the market shares of the three or five main players and the concentration of the market. Readers could also get the production, revenue, and average price shares of manufacturers.

Key Players: The report provides company profiles for a decent number of leading players in the Quantum Computing market. It shows your current and future market growth taking into account price, gross margin, income, production, service areas, production locations and other factors.

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Quantum Computing Market 2020 Break Down by Top Companies, Applications, Challenges, Opportunities and Forecast 2026 Cole Reports - Cole of Duty

Top 4 Emerging Technologies Of The Past And Present Decade To Know About – Thrive Global

The pace at which technology is evolving in this 21st century is mind-blowing. It could be a herculean task catching up with all the trends as they seem to be evolving at lightspeed. Especially for those who just newly started getting interested into those subjects.

Among top technologies that have gained huge attention and recognition in the past and present decade; Blockchain, AI, Quantum and 5G technologies seem to be erupting in a more disruptive manner. Today Ive decided to take an in-depth look at each of these technologies.

The technologies are leading the way for industry 4.0. They are attempting to bridge the gap between work inefficiencies and bloated workforce, and ultimately making life easier for everyone. It provides industries with the needed competitive edge.

These cutting-edge technologies are what is needed to streamline work processes, create unimaginable synergies and solve real-time problems. This is true to the extent at which you are able to leverage these technologies to build your businesses or use these technologies in your day to day business. Most beneficially they surely will be used in big industries and the government sector. Probably to a point where these technologies can start to become scary and shall not be tolerated, but that is another subject to separately write about.

The uniqueness of these technologies is that they enact on data in different ways, the possibilities are endless when two or more of these technologies are combined.

Many people think about blockchain technology with regard to cryptocurrencies like Bitcoin. Blockchain, however, has found increasing use-cases across various industries. It has seen wide range applications across most industries where security and privacy are of utmost importance, thanks to blockchain immutability and tamper-proof ledger.

The positive impact of blockchain has been felt in almost all spheres of human endeavors. Even though blockchain is more pronounced in the financial and service payment sector, its impact, however, is fast rising in other sectors such as healthcare, supply chain, security, politics, real estate, legal industry, education, etc.

Blockchain is the most innovative technology of the present decade because of its promise of financial bureaucracy. Blockchain transparency has the potential of reducing the degree of fraudulent practices, data reveals that banks and other financial institutions lose close to $4 trillion to cyber-theft every year. Blockchain has the potentials to put an end to this.

Blockchain-related jobs have also been identified as the second-fastest growing category of jobs, with over 14 job openings for every one blockchain developer. Picking up a career in this sector looks very lucrative. It was estimated that global business value will rise to $3.1 trillion in 2030 as a result of the implementation of blockchain technology.

Businesses that fail to embrace this technology might stand the risk of becoming redundant. Using blockchain can have just unlimited scenarios and advantages if applied right, the most important problem which a blockchain can solve is the issue of trust.

Artificial Intelligence is the simulation of a human-like intelligence through computer systems. Usually, these computer systems are programmed in a way to mimic human-like actions. Due to the complex nature of human activities, the simulation process is nevertheless complicated as well.

Since the AI technology came to limelight, it has been breaking new grounds in almost all areas of human endeavors. A lot of speculations have it that AI will seize a larger part of the workforce, help entrepreneurs cut costs, and automate business processes. In some big industries AI is already fully operating and making its limited, but super accurate decisions.

Recent AI developments have contributed to major advancements in the world of medicine. In medicine, Artificial intelligence will be able to improve cancer diagnosis and prevent around 22,000 deaths a year by 2033.

In business, AI is aiding managers in business analytics and faster decision making. AI is also touching base with the professional and corporate workforce through the automation of business processes.

PwC predicts that by 2030 AI will rake in about $15.7 trillion to the world economy, causing world GDP to shoot up by 14%. It will achieve these feet by ultimately improving business processes, cutting down cost and increasing work efficiency. Canadian Genius Entrepreneur Geordie Rose who is the founder of Kindred AI believes that by 2030 AI will be more intelligent than all of us humans. He goes that far that he says that a super-smart AI can be seen like another entity, like a kind of a digital species or in his words like a super-intelligent Alien. A very informative Youtube Video where Geordie talks to students in a Vancouver university with the target of recruiting some geniuses.

A 4G enabled-device will let you download a 2-hour video in 3 minutes, with a 5G device, you will do the same in just 3 seconds. However, this technology is not just about downloading movies.

When 5G rolls out more widely in the coming years, it will accelerate the production of more sophisticated applications to address problems and improve industry-wide innovations. This technology is poised to provide wireless communication at the speed and latency required for complex applications in IoT devices.

When globally implemented, 5G can enable emerging markets to reach the same pace of operation as their already established counterparts. Service providers creating 5G-based solutions for business-specific applications will then have valuable advantage early-adoption.

Telecom networks such as Qualcomm, Huawei, AT&T, Verizon, and Nokia are competing on who will lead the pace of 5G development.

There are lot of conspiracies with health issues about 5G and recently seen videos of people damaging the 5G Antennas.

Quantum technology encompasses far more than just quantum computing, this is going to become the next wave of superfast personal and commercial computers.

Nonetheless, the broader category of quantum innovations exploits the strange behavior of small particles for a wide range of applications, including navigation aids, advanced imaging technology, and extremely accurate timing systems.

This technology has also found increased applications in communication, cryptography, sensors, and measurement instruments. Businesses within this space will be looking at adopting quantum technology to transform the way they enact on objects.

IBM explains in this wonderful 4-minute video what exactly the difference is between the normal ways of binary computing and the advanced way of computing with qubits and how they exactly work to solve a calculation.

2020 Looks Promising

The pace of the emergence of new technologies is leading the 21st-century innovation and creating myriad opportunities for businesses to expand and enhance service delivery at the ideal time. These four technologies are certainly taking the lead in providing endless benefits both in the short and the long run. Watch out, keep researching and stay tuned.

Matthias Mende

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Top 4 Emerging Technologies Of The Past And Present Decade To Know About - Thrive Global

How will the coronavirus change our lives? – Fast Company

Were four weeks into the massive time-out forced on us by coronavirus. Many of us have spent much of that time trying to get used to the radical lifestyle change the virus has brought. But were also beginning to think about the end of the crisis, and what the world will look like afterward.

So its a good time to round up some opinions about how the pandemic might change how we think about various aspects of life and work. We asked some executives, venture capitalists, and analysts for thoughts on the specific changes they expected to see in their worlds.

Naturally, many of them tended to see the aftermath of the COVID-19 crisis in optimistic terms, at least when it comes to their own products, ideas, and causes. And at least some of them are probably right. But the general themes in their comments add up to preview of what might be ahead for tech companies and consumers once the virus is no longer the biggest news story in the world.

The responses below have been edited for publication.

Matthew Prince, CEO of CloudflareThe pandemic has resulted in what is effectively the largest work from home experiment ever conducted in human history . . . Were seeing the effect on the internet, in terms of traffic patterns that are shifting. People are accessing more educational resources online for their kids; finding unconventional ways to connect with coworkers, friends, and family; and employers are being more flexible in how they respond to employee needs through more dynamic, cloud-based technology. I think well see these shifts last well beyond the immediate fallout of the COVID-19 outbreak.

Jared Spataro, corporate vice president, Microsoft 365This time will go down as a turning point for the way people work and learn. We have a time machine as China navigates its return back to workand were not seeing usage of Microsoft Teams dip. People are carrying what they learned and experienced from remote work back to their new normal. Were learning so much about sustained remote work during this time.

Remote hiring of technical talent will become the norm.

Tim Bajarin, principal analyst at Creative StrategiesWe talked to CIOs recently, and they told us that they are becoming more comfortable with at least some of their staff working from home. Two CIOs even quantified it by saying they might consider letting as much as 25% of their staff work from home. That would mean less people in the office, and in turn, possibly less demand for office space. I believe that this could signal the death of open space work environments. The experience with COVID-19 will for years make people more aware of working in shoulder-to-shoulder open offices where it is easy for viruses to spread.

Eva Chen, CEO at Trend MicroThe COVID-19 experience will . . . build our courage to adopt new patterns to fix antiquated processes. As a result, organizations will ditch the notion of having a big office and revert back to a small-town model of working in cluster offices with more remote work. Even more so, company headquarters will be located in the cloud, shifting how we protect enterprise data in the virtual cloud and how we secure data from more diverse endpoints.

Sampriti Ganguli, CEO of the social venture firm Arabella AdvisorsWe are . . . all becoming BBC Man, meaning our kids and dogs routinely rush our meetings. Weve probably crossed the chasm between what is acceptable in the office and what is acceptable at home, and in many ways, these more intimate moments allow us to have deeper and more meaningful connections as humans. I dont think were going back to a world of working mostly from offices anytime soon, and as such, there are new business norms that work for home and work.

Steve Case, cofounder AOL, CEO and chairman of Revolution[We] believe the COVID-19 pandemic will encourage peopleentrepreneurs, investors, and employeesto consider opportunities outside of the coastal tech hubs. People who have been considering a move, to tap into the sector expertise (healthcare, food and agriculture, etc.) that exists in many parts of the country, or for a lifestyle change, or to be near family and friends, may choose this moment to relocate, accelerating a talent boomerang, and helping emerging startup cities rise. On top of that, the increased willingness to accept remote working as a viable arrangement following this prolonged work-from-home period will further propel this trend.

Vivek Ravisankar, CEO and cofounder of programming-challenge platform HackerRankRemote hiring of technical talent will become the norm, accelerated by the normalization of remote work. This is a win-win for the economy and the talent pool, as it allows companies to fill positions quickly with qualified talent and opens up high-paying tech positions to developers everywhere. We were already seeing the shift toward prioritizing skills over pedigree in hiring. That will now evolve to skills over geography, making our tech talent pool more diverse, and our businesses and economy stronger.

AJ Shankar, CEO and cofounder of EverlawIn the modern work environment, real-time communication mediums like chat allow for a certain blurring of the line between personal life and work life, an always-on mentality. But now, in a COVID world, that line has never been more blurred: There is no physical separation at all. So I predict that expectations around availability will changefor the better. For employee-friendly companies, evening hours will ultimately revert to family or personal time, as they should. This wont happen automatically; a change in mindset and process is required.

Stan Chudnovsky, VP of Messenger, FacebookIts becoming more clear every day that the way people are using technology to spend quality time with loved ones, engage with businesses, and perform their jobs is fundamentally shifting to a new normal. Loved ones who hadnt seen each other in years are now seeing each other daily, people are getting creative with virtual happy hours and keeping up with their formerly physical lives with shared workouts and virtual birthday parties on products like Messenger. Of course, there will be some tough consequences when we come out the other side of this, but I believe the growing acceptance of technology to help us feel connected will have lasting benefits.

Michael Hendrix, partner and global design director, IdeoRight now, the virus seems like an accelerator for digital change that was already underway . . . the surprise has been to see the resistance to this digital change suddenly evaporate. What organizations resisted for a decade is now core to survival and innovation. It is exciting, because this digital mindset will persist, and it is highly unlikely companies will try to return to what worked prior to the pandemic.

We could get to a state of nearly universal online access at home.

Alex Farr, founder and CEO of voice tech company ZammoUsing videoconferencing is not only going to become a more common part of life due to this pandemicthe way it shows up through our tech devices will multiply. At work and at home, well ask voice assistants to call our client, our boss, our mom, our friends, and on command, Alexa, Google Assistant, Siri, etc., will take us right to those live video conversations.

Will Cathcart, head of WhatsAppAs people have been forced physically apart weve seen them make far more video calls on WhatsApp than ever before. These are intimate and private conversations that people expect no one else should seeno different than if you were talking in person. Not criminals, not hackers, not even a company. I believe that our shared experience of being physically isolated from one another will cause us to appreciate and value the privacy and security that comes with end-to-end encryption even more than we did before.

Simon Allen, CEO of McGraw-HillThe change we are seeing right now in education is not something that is likely to revert back to normal in the fall. Although teachers will always be integral to the education process, there will need to be continued flexibility and agility when it comes to things like the delivery of content, testing, and grading. I expect that we will see an increase in blended learning environments that include learning in both the physical classroom setting and online.

Adam Enbar, CEO of Flatiron SchoolRight now, educators are relying on Zoom and Slack to teach and engage with students. Were realizing its falling short in replicating the classroom experience, but the truth is that it was never meant to be a substitute. In fact, no ed-tech tool or platform can or should replicate the in-person classroom; techs role is to create new experiences altogether. Nothing spurs innovation like people experiencing problems. When things are back to normal, Zoom and Slack usage will go downand thats okay. Instead, well see a boom in technology that is built by entrepreneurs looking to create entirely new experiences custom to the remote education or work experience.

Sal Khan, founder and CEO of educational nonprofit Khan AcademyThe need for online access and devices in every home is now so dire that it may finally mobilize society to treat internet connectivity as a must-have rather than a nice-to-have. Were already seeing governments, school districts, philanthropists, and corporations step up to close the digital divide. If this continues to happen, we could get to a state of nearly universal online access at home.

Dr. Claire Novorol, cofounder and chief medical officer, Ada HealthThe adoption of digital health toolsfrom assessment services to telemedicinehas rapidly accelerated, with healthcare organizations across the world looking to digital solutions to support their efforts against the pandemic, and health tech companies keen to rise to the occasion in support of healthcare payers, providers and patients alike. Its clear that we are witnessing a step-change in the adoption of digital health solutions, and that this has long-term potential. The healthcare industry will be greatly affected by the coronavirus pandemic, and we can expect digital health technologies to form an essential part of the way forward.

Pat Combes, worldwide technical leader, healthcare and life sciences at AWSThe biggest barrier to ensuring doctors have the most complete medical history on any patient, at every point of their care, is the lack of interoperability among systems, preventing data and electronic health records from following a patient throughout their care journey. Bringing this information together is a manual and time-consuming process. But, this is one of those pivotal moments in time when we have an opportunity to identify and work to fix the underlying problems that plague our system, with so many researchers, health systems, governments, and enterprises pooling efforts and data to better understand and combat COVID-19.

Ara Katz, cofounder and co-CEO, Seed HealthAt a time when misinformation is especially rampant, and in many recent cases, dangerous, it is imperative that those working in science collectively steward and uphold a standard for how information is translated and shared to the public. COVID-19 is a reminder of how science informs decisions, shapes policy, and can save lives. The antidote to this current infodemic may be as important to our collective future as a vaccine.

Harry Ritter, founder and CEO of wellness professional community AlmaThere will be a monumental shift in attitudes toward mental health. [S]ociety, having experienced this collective trauma and grief, will develop new levels of empathy and a willingness to talk about mental healthcare as an essential part of healthcare in ways we have not seen before. Employers are already seeing how emotional well-being is factoring into their workforces ability to perform under stress. Ideally they will come out of this better able to recognize their obligation to prioritize mental healthcare as an employee benefit.

Peter Chapman, CEO and president, quantum-computing company IonQWithin the next 12 to 18 months, were expecting quantum computers to start to routinely solve problems that supercomputers and cloud computing cannot. When humanity faces the next pandemic, Im hopeful that a quantum computer will be able to model the virus, its interactions within the human body that will drive possible solutions, and limit the future economic damage and human suffering.

David Barrett, CEO and founder of ExpensifyThe COVID-19 crisis has swiftly exposed the fragile underbellies of many companies, especially those in tech that have been propped up by huge funding rounds and strategies that require massive monthly burn rates. Theyre now teetering on the edge of collapse, with most facing layoffs across the board and some searching for buyers as a last resort. On the other hand, profitable companies . . . are simply tightening their belts and carrying on with business (mostly) as usual. Going forward, investors mindsets and qualifications about what constitutes a truly valuable company will change. Rather than focusing on the quantitative aspects like funding rounds and revenue, investors will place a greater emphasis on the qualitative aspects, such as an organizations structure, team, culture, flexibility, and profitability.

Restaurants might permanently link up with delivery service platforms or expand their reach via ghost kitchens.

Michael Masserman, global head of policy and social impact, LyftAs we look to the reopening of cities, people will be looking for affordable, reliable ways to stay socially distant while commuting, including turning to transportation options such as rideshare, bike share, and scooters. There will also be an opportunity for local governments, as well as key advocates and stakeholders, to consider reshaping our cities to be built around people and not cars.

Avi Meir, cofounder and CEO, TravelPerkCountries and regions will emerge from lockdown at different paces, leading to corridors of travel between destinations opening back up one by one. Were already beginning to see early signs of a modest pickup in travel again in Asia Pacific, as the local pressure of the virus lessens. When travel does begin to resume, domestic travel will be first. For most countries, that means taking a train, not least because theyre less crowded.

Ed Barriball, who leads McKinseys Public Sector Practice in North AmericaIn the short term, companies are concerned about the shortages of critical goods across the supply chain, and some are looking for alternative sources closer to home. In the long term, once we emerge from the current crisis, we expect businesses and governments to focus on better quantifying the risks faced and incorporating potential losses into business cases. These businesses will model the size and impact of various shock scenarios to determine actions they should take to rebuild their supply chains and simultaneously build resilience for the future. These actions could include bringing suppliers closer to home but could also include a range of other resilience investments.

Amar Hanspal, former CEO at Autodesk and now CEO at Bright MachinesThis pandemic will have a lasting impact . . . on the way physical products are made. Customers I talk to are grappling with supply chain and factory disruptions across the globe. This has been a wake-up call to manufacturers. The current way of building products in centralized factories with low-cost labor halfway around the world simply cant weather storms of uncertainty. Moving forward, factories and supply chains will require, and businesses will mandate, much more resilient manufacturing through nearshoring and even onshoring, full automation, and software-based management.

Sarah Stein Greenberg, executive director of the Stanford d.schoolIn times of great uncertainty, the most critical skill is to be able to adapt as conditions change. This is a kind of ambidexterity: focusing on surviving in the current moment while you also build toward thriving in a future that will look different. To get there, successful leaders are creating and holding space in organizations for people to be generative, despite the challenging and stressful environment. Drawing from one of the fundamental strengths of design: by separating the process of generating ideas from critiquing and selecting them, we are seeing organizations and individuals rewarded with a far wider range of potential solutions.

Will Lopez, head of accountant community at HR platform GustoCOVID-19 isnt the end of brick-and-mortar storestheyre vital to our communities and our economybut the way they operate will change. This crisis will force small businesses that have historically relied on foot traffic as their main source of income to develop alternative revenue streams so they can weather the next major event. For example, many restaurants might permanently link up with delivery service platforms or expand their geographic reach via ghost kitchens, and more boutiques will develop an online presence that reaches beyond their local neighborhoods.

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How will the coronavirus change our lives? - Fast Company

Pentagon wants commercial, space-based quantum sensors within 2 years – The Sociable

The Pentagons Defense Innovation Unit is looking to the private sector to develop space-based quantum sensing prototypes within two years the kind of sensors that could contribute to a space-based quantum internet.

Highlights:

Quantum technologies will render all previously existing stealth, encryption, and communications technologies obsolete, so naturally the Pentagon wants to develop quantum technologies as a matter of national security.

The Defense Innovation Unit (DIU) has opened a solicitation to evaluate commercial solutions that utilize demonstrable quantum technology to achieve significant performance improvements for aerospace and other novel applications to include, but not limited to, inertial sensing, timing and gravimetry.

The DIU wants a prototype within 24 months that consists of acompact, high-performance quantum sensor for precision inertial measurement in deep space and other GPS-denied environments.

There are a lot of technical concepts that go into this technology, but for simplicitys sake, the DIU is looking for quantum sensing technology that can perform accurate measurements by overcoming the effects of gravity on time and space.

While the DIU did not go into any specifics about what the quantum sensing technology would actually be used for, we may gleam some ideas from what the military has already been researching specifically improved communications, precision navigation, and precision timing.

For example, the Air Force Research Laboratory has been investigating a variety of quantum-based sensors to create secure, jam-resistant alternatives to GPS, according to National Defense Magazine.

And because quantum sensors can detect radar signatures and beyond, they may be used by the military tobypass just about any stealth technology.

Other potential applications could include Earth defense mechanisms that could detect, prevent, or respond to missile attacks, asteroids, and comets, as well as keeping track of satellites and space debris that whiz around Earths orbit.

Additionally, a network of quantum technologies could offer the military security, sensing and timekeeping capabilities not possible with traditional networking approaches, according to the US Army Research Laboratory.

If we take the idea of quantum sensors a step further and into the realm of quantum sensing networks, then we are looking at one component of a quantum internet, when combined with quantum computing.

A quantum internet will be the platform of a quantum ecosystem, where computers, networks, and sensors exchange information in a fundamentally new manner where sensing, communication, and computing literally work together as one entity, Argonne Laboratory senior scientistDavid Awschalom told How Stuff Works.

The notion of a space-based quantum internet using satellite constellations is becoming even more enticing, as evidenced in the joint research paper, Spooky Action at a Global Distance Resource-Rate Analysis of a Space-Based Entanglement-Distribution Network for the Quantum Internet.

According to the scientists, Recent experimental breakthroughs in satellite quantum communications have opened up the possibility of creating a global quantum internet using satellite links, and, This approach appears to be particularly viable in the near term.

The paper seems to describe quantum technologies that are nearly identical to the ones the DIU is looking to build.

Aquantum internet would allow for the execution of other quantum-information-processing tasks, such as quantum teleportation, quantum clock synchronization, distributed quantum computation, and distributedquantum metrology and sensing, it reads.

SpaceX is already building a space-based internet through its Starlink program. Starlink looks to have 12,000 satellites orbiting the earth in a constellation that will beam high-speed internet to even the most remote parts of the planet.

The company led by Elon Musk has already launched some 360 satellites as part of the Starlink constellation.

All the news reports say that Starlink will provide either high-speed or broadband internet, and there are no mentions of SpaceX building a quantum internet, but the idea is an intriguing one.

SpaceX is already working with the Pentagon, the Air Force, NASA, and other government and defense entities.

In 2018, SpaceX won a $28.7 million fixed-price contract from the Air Force Research Laboratory for experiments in data connectivity involving ground sites, aircraft and space assets a project that could give a boost to the companys Starlink broadband satellite service, according to GeekWire.

Lets recap:

By the looks of it, the DIUs space-based quantum sensing prototypes could very well be components of a space-based quantum internet.

However, there has been no announcement from SpaceX saying that Starlink will be beaming down a quantum internet.

At any rate, well soon be looking at high-speed, broadband internet from above in the near future, quantum or otherwise.

Quantum computing: collaboration with the multiverse?

US Energy Dept lays foundation for quantum internet, funds $625M to establish quantum research centers over 5 years

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Pentagon wants commercial, space-based quantum sensors within 2 years - The Sociable

Developers: This new tool spots critical security bugs 97% of the time – TechRepublic

Microsoft claims a machine learning models its built for software developers can distinguish between security and non-security bugs 99% of the time.

Microsoft plans to open-source the methodology behind a machine learning algorithm that it claims can distinguish between security bugs and non-security bugs with 99% accuracy.

The company developed a machine learning model to help software developers more easily spot security issues and identify which ones need to prioritized.

By pairing the system with human security experts, Microsoft said it was able to develop an algorithm that was not only able to correctly identify security bugs with nearly 100% accuracy, but also correctly flag critical, high priority bugs 97% of the time.

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The company plans to open-source its methodology on GitHub "in the coming months".

According to Microsoft, its team of 47,000 developers generate some 30,000 bugs every month across its AzureDevOps and GitHub silos, causing headaches for security teams whose job it is to ensure critical security vulnerabilities don't go missed.

While tools that automatically flag and triaged bugs are available, sometimes false-positives are tagged or bugs are classified as low-impact issues when they are in fact more severe.

To remedy this, Microsoft set to work building a machine learning model capable of both classifying bugs as security or non-security issues, as well as identifying critical and non-critical bugs "with a level of accuracy that is as close as possible to that of a security expert."

This first involved feeding the model training data that had been approved by security experts, based on statistical sampling of security and non-security bugs. Once the production model had been approved, Microsoft set about programming a two-step learning model that would enable the algorithm to learn how to distinguish between security bugs and non-security bugs, and then assign labels to bugs indicating whether they were low-impact, important or critical.

Crucially, security experts were involved with the production model through every stage of the journey, reviewing and approving data to confirm labels were correct; selecting, training and evaluating modelling techniques; and manually reviewing random samples of bugs to assess the algorithm's accuracy.

Scott Christiansen, Senior Security Program Manager at Microsoft and Mayana Pereira, Microsoft Data and Applied Scientist, explained that the model was automatically re-trained with new data to it kept pace with the Microsoft's internal production cycle.

"The data is still approved by a security expert before the model is retrained, and we continuously monitor the number of bugs generated in production," they said.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99 percent of the time. The model is also 97 percent accurate at labeling critical and non-critical security bugs.

"This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited."

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Developers: This new tool spots critical security bugs 97% of the time - TechRepublic

Microsoft: Our AI can spot security flaws from just the titles of developers’ bug reports – ZDNet

Microsoft has revealed how it's applying machine learning to the challenge of correctly identifying which bug reports are actually security-related.

Its goal is to correctly identify security bugs at scale using a machine-learning model to analyze just the label of bug reports.

According to Microsoft, its 47,000 developers generate about 30,000 bugs a month, but only some of the flaws have security implications that need to be addressed during the development cycle.

Microsoft says its machine-learning model correctly distinguishes between security and non-security bugs 99% of the time. It can also accurately identify critical security bugs 97% of the time.

SEE: 10 tips for new cybersecurity pros (free PDF)

The model allows Microsoft to label and prioritize bugs without necessarily throwing more human resources at the challenge. Fortunately for Microsoft, it has a trove of 13 million work items and bugs it's collected since 2001 to train its machine-learning model on.

Microsoft used a supervised learning approach to teach a machine-learning model how to classify data from pre-labeled data and then used that model to label data that wasn't already classified.

Importantly, the classifier is able to classify bug reports just from the title of the bug report, allowing it to get around the problem of handling sensitive information within bug reports such as passwords or personal information.

"We train classifiers for the identification of security bug reports (SBRs) based solely on the title of the reports," explain Mayana Pereira, a Microsoft data scientist, and Scott Christiansen from Microsoft's Customer Security and Trust division in a new paper titled Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data.

"To the best of our knowledge this is the first work to do so. Previous works either used the complete bug report or enhanced the bug report with additional complementary features," they write.

"Classifying bugs based solely on the tile is particularly relevant when the complete bug reports cannot be made available due to privacy concerns. For example, it is notorious the case of bug reports that contain passwords and other sensitive data."

SEE: Zoom vs Microsoft Teams? Now even Parliament is trying to decide

Microsoft still relies on security experts who are involved in training, retraining, and evaluating the model, as well as approving training data that its data scientists fed into the machine-learning model.

"By applying machine learning to our data, we accurately classify which work items are security bugs 99% of the time. The model is also 97% accurate at labeling critical and non-critical security bugs. This level of accuracy gives us confidence that we are catching more security vulnerabilities before they are exploited," Pereira and Christiansen said in a blogpost.

Microsoft plans to share its methodology on GitHub in the coming months.

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Microsoft: Our AI can spot security flaws from just the titles of developers' bug reports - ZDNet

Who knows the secret of the black magic box? Boffins seek the secrets of AI learning by mapping digital neurons – The Register

Roundup OpenAI Microscope: Neural networks, often described as black boxes, are complicated; its difficult to understand how all the neurons in the different layers interact with one another. As a result, machine learning engineers have a hard time trying to interpret their models.

OpenAI Microscope, a new project launched this week, shows that it is possible to see which groups of neurons are activated in a model when it processes an image. In other words, its possible to see what features these neurons in the different layers are learning. For example, the tools show what parts of a neural network are looking at the wheels or the windows in an image of a car.

There are eight different visualisations that take you through eight popular models - you can explore them all here.

At the moment, its more of an educational resource. The Microscope tools wont help you interpret your own models because they cant be applied to custom neural networks.

Generating the millions of images and underlying data for a Microscope visualization requires running lots of distributed jobs, OpenAI explained. At present, our tooling for doing this isn't usable by anyone other than us and is entangled with other infrastructure.

The researchers hope that their visualisation tools might inspire people to study the connections between neurons. Were excited to see how the community will use Microscope, and we encourage you to reuse these assets. In particular, we think it has a lot of potential in supporting the Circuits collaborationa project to reverse engineer neural networks by analyzing individual neurons and their connectionsor similar work, it concluded.

Don't stand so close to me: Current social distancing guidelines require people to stay at least six feet away from each other to prevent the spread of the novel coronavirus.

But how do you enforce this rule? Well, you cant really but you can try. Landing AI, a Silicon Valley startup led by Andrew Ng, has built what it calls an AI-enabled social distancing detection tool.

Heres how it works: Machine learning software analyses camera footage of people walking around and translates the frames into a birds eye view, where each person is represented as a green dot. A calibration tool estimates how far apart these people or dots are from one another by counting the pixels between them in the images. If theyre less than six feet apart, the dots turn red.

Landing AI said it built the tool to help the manufacturing and pharmaceutical industries. For example, at a factory that produces protective equipment, technicians could integrate this software into their security camera systems to monitor the working environment with easy calibration steps, it said.

The detector could highlight people whose distance is below the minimum acceptable distance in red, and draw a line between to emphasize this. The system will also be able to issue an alert to remind people to keep a safe distance if the protocol is violated.

Landing AI built this prototype at the request of customers whose businesses are deemed essential during this time, a spokesperson told The Register.

The productionization of this system is still early and we are exploring a few ways to notify people when the social distancing protocol is not followed. The methods being explored include issuing an audible alert if people pass too closely to each other on the factory floor, and a nightly report that can help managers get additional insights into their team so that they can make decisions like rearranging the workspace if needed.

You can read more about the prototype here.

Amazon improves Alexas reading voice: Amazon has added a new speaking style for its digital assistant Alexa.

The long-form speaking style will supposedly make Alexa sound more natural when its reading webpages or articles aloud. The feature, built from a text-to-speech AI model, introduces more natural pauses as it recites paragraphs of text or switches from one character to another in dialogues.

Unfortunately, this function is only available for customers in the US at the moment. To learn how to implement the long-form speaking style, follow the rules here.

Zoox settles with Tesla over IP use: Self-driving car startup Zoox announced it had settled its lawsuit with Tesla and agreed to pay Musks auto biz damages of an undisclosed fee.

Zoox acknowledges that certain of its new hires from Tesla were in possession of Tesla documents pertaining to shipping, receiving, and warehouse procedures when they joined Zooxs logistics team, and Zoox regrets the actions of those employees, according to a statement. As part of the settlement, Zoox will also conduct enhanced confidentiality training to ensure that all Zoox employees are aware of and respect their confidentiality obligations.

The case [PDF], initially filed by Teslas lawyers last year, accused the startup and four of its employees of stealing proprietary documents describing its warehouses and operations, and attempting to get more of its employees to join Zoox.

NeurIPS deadline extended: Heres a bit of good news for AI researchers amid all the doom and gloom of the current coronavirus pandemic: the deadline for submitting research papers to the annual NeurIPS AI conference has been extended.

Now, academics have until 27 May to submit their abstracts and 3 June to submit their finished papers. It can be hard to work during current lockdown situations as people juggle looking after children and their jobs.

Due to continued COVID-19 disruption, we have decided to extend the NeurIPS submission deadline by just over three weeks, the program chairs announced this week.

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Who knows the secret of the black magic box? Boffins seek the secrets of AI learning by mapping digital neurons - The Register

OnDemand Webinar | Embracing Machine Learning & Intelligence to Improve Threat Hunting & Detection – BankInfoSecurity.com

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SAP Makes Support Experience Even Smarter With ML and AI – AiThority

SAP SE announced several updates, including the Schedule a ManagerandAsk an Expert Peerservices, to its Next-Generation Support approach focused on the customer support experience and enabling customer success. Based on artificial intelligence (AI) and machine learning technologies, SAP has further developed existing functionalities with new, automated capabilities such as theIncident Solution Matching service and automatic translation.

When it comes to customer support, weve seen great success in flipping the customer engagement model by leveraging AI and machine learning technologies across our product support functionalities and solutions, saidAndreas Heckmann, head of Customer Solution Support and Innovation and executive vice president, SAP. To simplify and enhance the customer experience through our award-winning support channels, were making huge steps towards our goal of meeting customers needs by anticipating what they may need before it even occurs.

Recommended AI News: Kofax Presents Partner of the Year Awards

AI and machine learning technologies are key to improving and simplifying the customer support experience. They continue to play an important role in expanding Next-Generation Support to help SAP deliver maximum business outcomes for customers. SAP has expanded its offerings by adding new features to existing services, for example:

Recommended AI News: Kyocera Selects Skyhook to Power Precision Location Services for Rugged DuraXV Extreme

Customers expect their issues to be resolved quickly, and SAP strives toward a consistent line of communication across all support channels, including real-time options. SAP continues to improve, innovate and extend live support for technical issues by connecting directly with customers to provide a personal customer experience. Building on top of live support services, such asExpert ChatandSchedule an Expert, SAP has made significant strides in upgrading its real-time support channels. For example, it now offers the Schedule a Manager service and a peer-to-peer collaboration channel through the Ask an Expert Peer service.

By continuing to invest in AI and machine learningbased technologies, SAP enables more efficient support processes for customers while providing the foundation for predictive support functionalities and superior customer support experiences.

Customers can learn more about the Next-Generation Support approach through theProduct Support Accreditation program, available to SAP customers and partners at no additional cost. Customers can be empowered to get the best out of SAPs product support tools and the Next-Generation Support approach.

Recommended AI News: O.C. Tanner Recognized as a Leader in Everest Group PEAK Matrix Rewards & Recognition Solutions Assessment 2020

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SAP Makes Support Experience Even Smarter With ML and AI - AiThority

Has Harvard’s David Sinclair Found the Fountain of Youth?

Health

Not yetbut he sure is getting rich, famous, and having a blast while trying.

Portrait by Ken Richardson

Like any dreamer, David Sinclair has a tendency to live in the future. The first time that thought crossed my mind, we were hurtling toward Worcester in his Tesla, on our way to visit one of his many companies working on an antidote to aging. Sinclair told me hed recently discovered, using a health-tracking device, that hes shaved a decade off his life: Biologically speaking, he is now 40, not 50. I took a good look at him. Except for the pillow he sat on while he drove, the wrinkles that formed around his eyes when he flashed his mischievous grin, and the note scrawled on the back of his hand (lest he forget something he has to do), there was no way in hell he looked anywhere near 50. He is slight of build, with nary a gray hair, and bears a passing resemblance to that forever child Alfred E. Neuman. He even says he feels like a kid, too.

I had skipped breakfast that morning to get a feel for what its like to be Sinclair, whose habit of not eating anything until the afternoonalong with ingesting a mysterious medley of pillsis one of his many life-extending practices. When I asked about one of the drugs he takes, he reached into his pocket and pulled out a capsule filled with a white powder that he packages himself in his lab. He has told reporters that the substance inside is a miracle molecule. I plucked it from his hand and put it in my own. It felt so light in my palm. So easy to believe. And that is precisely the problem.

From time immemorial, people have been on a fantastical quest for a substance that would extend life, or even grant immortality. The medieval alchemists sought the elixir of life. Explorer Ponce de Leon looked for the fountain of youth in what is now the southern United States but, in an ironic twist of fate, found Florida, a place where people go to grow old and die. As the centuries wore on, traffic in life-extending substances and practices became the clear bailiwick of snake-oil salesmen, charlatans, and quacks.

More recently, though, longevity has become the stuff of legitimate science. Sinclair is a superstar among a group of researchers who have harnessed science and technologys latest advances in an effort to parse out, for the very first time, the biological mechanisms of aging in hopes of slowing or even reversing the process. The goal of this field is not to make us young for youths sake, but to address the single greatest risk factor for heart disease, cancer, diabetes, dementia, and many other forms of modern-day suffering: aging. This radical new thinking about medicine maintains that if we can address the upstream cause of these diseases, we can cure them all at once (instead of relying on the current Whack-a-Mole approach) and increase the number of years people live with good health. But it is also true, experts say, that eliminating all of these diseases of aging will make people live longer. We are on the verge of a public health breakthrough of the kind we have never seen before, says S. Jay Olshansky, a professor of public health who studies demographics and aging at the University of Illinois at Chicago. It is not trivial. This is bigtime.

Sinclair deserves much of the credit for getting the field to where it is today. The Australian-born Harvard Medical School professor of genetics has had countless discoveries published in the most respected scientific journals in the world and has received dozens of scientific prizes and honors. Last year he was appointed an Officer of the Order of Australia for his contributions to humanity. Wealthy investors, including WeWork cofounder Adam Neumann, have bet hundreds of millions of dollars on his science and invested in the 17 companies hes founded. When Sinclairs book, Lifespan: Why We Ageand Why We Dont Have To, was released in September, it reached number 11 on the New York Times bestseller list in just over a week.

At the same time, Sinclair is one of sciences most controversial figures, regarded by many as a slick salesman who overhypes his work and its potential. Some critics cringe when he speaks of miracle molecules and everlasting life. Others whisper that his science may not be completely sound. Still others roll their eyes over his habit of taking drugs that havent been proven to delay aging in anyone who isnt a mouse. The prevailing wish among his doubters is for him to simply keep his mouth shut. He is a complicated guy, says Steven Austad, a professor of biology who studies aging at the University of Alabama at Birmingham and is a friend of Sinclairs. Hes a superb scientist, as well as a superb salesman. You talk to him about science and you wont find many more knowledgeable, incisive experimentalists as David. And then you can listen to the stuff he says on TV and be like, What the hell is he talking about?

Sinclairs bold statements and pill-popping habits have ruffled feathers closer to home, tooat the very institution that employs him. He does do research and he gets it published in peer-reviewed journals, and if he just did that, itd be fine, says a Harvard Medical School professor who asked to remain anonymous. But then he speaks out about how he makes himself young and says stuff that would be embarrassing for any normal scientist to say.

In other words, in an increasingly legitimate field of science desperate to distance itself from the alchemists and quacks of yore, Sinclair presents somewhat of a problem. As a brilliant scientist in the lab, he is a major asset to his fields eternal quest for legitimacy. Let loose in the world, though, the self-described Star Trek wannabe, whos eager for the future to arrive as fast as possible, is somewhat of a liability. He may very well be the man who will unlock the secret to extending life some 10, 20, or even 30 yearsso long as he doesnt get lost searching for the fountain of youth along the way.

David Sinclair hanging out with Joe Rogan after appearing on his podcast. / Photo from Instagram

Sinclair can remember with startling clarity the day he first learned about death. He was with his beloved grandmother at her home in Turramurra, a leafy suburb of Sydney on the edge of the bush. They were seated on the floor playing when she told him his cat would only live to about 15. He was shocked. And the news only got worse. Everybody dies, she told him.

It is not surprising for children to be disturbed when they learn about mortality, but most of them move on, squirreling away the fear and dread until it comes bubbling back to the surface with the appearance of gray hairs, knee pain, and mental lacunas. Sinclairs trajectory was slightly different. In a sense, he never got over it.

While his biochemist parents worked, Sinclair spent most of his childhood with his fun-loving, free-spirited grandmother, who admonished him to never grow up. By the time he enrolled at the University of New South Wales to study biochemistry, he was convinced that science would one day catch up with his grandmothers ideas and people would be able to stay young forever. He believed, however, that he had been born too early to see it. He told his friends at school over coffee that they were likely to be the last of thousands of generations to live the sad existence of such a short life. But no sooner had he thought it, he says, than he considered the fact that maybe he was wrong. Maybe it could happen in his lifetime, and maybe he could be a part of it. Sinclair had found his lifes purpose.

His next stop was 10,000 miles away at MIT, where at the tender age of 24 he became a postdoc in the lab of Leonard Guarente, who had just started studying aging in yeast. Sinclairs colleagues remember him as someone who was aggressive, ambitious, and tireless: He was often the first to come into the lab and stayed as long as he could before dashing to catch the last train of the night. His colleague Shin-ichiro Imai, a professor of developmental biology at the Washington University School of Medicine who first met Sinclair in Guarentes lab, says Sinclair had a keen eye for capturing novel concepts and, based on that foundation, building new lines of research faster than anyone else.

At the time, aging research, once considered a fringe science, was still in its infancy, but Sinclair was determined to propel it to legitimacy. Three years into his time at MIT, he made a groundbreaking discovery that explained, for the first time, a mechanism of aging in yeast and opened up the possibility of one day manipulating the process in humans.

From there, Sinclairs career took off like a rocket. He soon left MIT to run his own lab at Harvard Medical School and became an assistant professor of genetics, continuing to build on discoveries made at Guarentes lab about sirtuins, a family of proteins that exists in all living beings. These proteins are usually dormant, but when activated through stressors (such as restricting calories), they can enhance health and extend life in yeast. Sinclair was determined to find a substance that could mimic the effects of restricting calories in yeast, something that could one day be turned into a medicine that cures aging.

True to form, he got to work, harder and faster than anyone else, Imai says. He screened some 20,000 substances until, one day, his collaborator called to say that hed gotten a hit: resveratrol, a molecule found in red wine that has long been suspected to play a role in human health. Sinclair couldnt believe what he was hearing and knew others wouldnt, either. So he set out to disprove the finding right on his dining room table, where he lined up a series of petri dishes filled with yeast that had been fed different substances. When he discovered that the dish with yeast that lived 50 percent longer had been fed resveratrol, he cried out to his wife, I think we have found something important here.

The discovery was the start of another phase in Sinclairs career, one in which wealthy investors played as much of a role as the scientific community. In 2004, with the help of serial biotech entrepreneur Christoph Westphal, he founded a company called Sirtris Pharmaceuticals to pursue clinical-stage drugs inspired by the resveratrol molecule. At the time, it was almost unheard of for a scientist in the aging field to start a company. David was a pioneer in merging academic and commercial research, Austad says. A lot of scientists would have liked to do what David did, but they didnt know how, or have the appropriate skills to raise the money and convince the investors that this science was promising a revolution in health. David did.

Meanwhile, in his lab, Sinclair pushed his studies up the evolutionary chain into mice, and in 2006 published the paper that would change his life: a study showing that overweight rodents fed resveratrol aged slower and stayed healthier than ones that did not consume the substance. It was an instant sensation, landing on the front page of the New York Times. Sinclair gave a few dozen interviews before sitting down, relaxed and charming, for the Charlie Rose show. A 60 Minutes special on resveratrol wasnt far behind, and soon he was telling Morley Safer we could expect an FDA-approved pill in five years time. Resveratrol, he once boasted to a reporter from the magazine Science, was as close to a miraculous molecule as you can find.

In no time, Sinclair went from being a scientist toiling away in a lab to someone whom strangers recognized on the street. He became a longevity guru to legions of people hoping to glean insight about how to forestall their own mortality. And, he became rich. Sirtris went public in 2007, and one year later, pharma giant GlaxoSmithKline snatched it up for an astounding $720 million. Resveratrol had made Sinclair famous and wealthy beyond what he had ever imagined, but it was also about to turn him into one of modern sciences most polarizing figures.

David Sinclair in his lab at Harvard Medical School. / Portrait by Ken Richardson

Sinclair was sitting at his desk at Harvard one day in 2010 when a colleague called to offer his heartfelt sympathies: Pfizer scientists had just released a paper essentially saying that Sinclairs work on sirtuins was bunk. When he finally got hold of the document himself, Sinclair couldnt believe his eyes. It wasnt clear to me at all that we were wrong, he told me. We had data that showed we were right.

And yet, it wasnt the first time Sinclairs science had been challenged. A couple of years after his initial groundbreaking yeast study on resveratrol, two of his former colleagues from Guarentes lab published a paper reporting on their inability to replicate it, suggesting his conclusions were wrong. A few years later, scientists from the pharma company Amgen also raised doubts, claiming Sinclairs findings were erroneous. The Pfizer paper, though, was different. Not only did one of the biggest pharma companies in the world claim he was wrong on resveratrol, it also stated his entire theory on sirtuins was completely off. In response, Sinclair publicly questioned whether the Pfizer scientists had made mistakes running their experimentwhich didnt exactly go over well. I was criticized for saying that Pfizer doesnt know how to make a molecule right, he explained.

As the scientific community continued to raise doubts and gossip behind his back, Sinclair sank to a dark place. I spent a week in bed, he told me. I couldnt get out. My lab shrunk to, like, four people. When I asked his assistant if she remembers what it was like when the Pfizer paper came out, she sighed, looked down, and shook her head from side to side: That was devastating.

Still, its hard to keep Sinclair down for long; after all, he lives by the very idea of never say die. When he finally got out of bed, he went back into the lab to prove his naysayers wrong. The day I visited his lab, he stood with his arms crossed and a look of satisfaction on his face as he showed me a framed copy of a 2013 scientific paper that he says settled the debate and proved he was right about resveratrol activating sirtuins. In it, he showed that when scientists genetically engineered cells to change a single amino acid on a sirtuin, resveratrol had no effect on the cells. In the control cells with intact sirtuins, however, resveratrol did have an effect.

Not everyone, though, was convinced. There are lots of people in the field who harbor suspicions [about Sinclairs science], one researcher told me. It is hard to explain how the same lab on multiple occasions over a decade or so can publish multiple pieces of data that other labs cant reproduce. Whats more, GlaxoSmithKline halted a Sirtris trial in humans because of potential negative side effects and then shut the company down altogether just five years after buying it. Today, resveratrol is known as the miracle drug that wasnt.

To Sinclairs credit, none of his scientific papers have ever been retractedand none of the people who spoke to me about their suspicions of Sinclair wanted their names used. One of them admitted that it might not be his data that critics object to, but rather the way Sinclair talks about his findings. While his colleagues in the aging field overwhelmingly stick to a safe script, describing their research as a quest to extend years of health, Sinclair talks freely and excitedly about extending mortality to 150 years by the end of the centuryto say nothing of death eventually becoming a rarityboth of which critics say there is zero science to support. From his exalted platform as a scientist featured on TV and in the New York Times, Sinclair is promising the world that one day soon well be able to get a shot that reverses aging, and when it wears off and the gray hairs sprout again, well simply get a booster. Does that sound like science fiction? Something that is very far out in the future? Sinclair asks readers in his book. Let me be clear: its not.

Even the title of his bookthe part that says we dont have to ageelicited an exasperated groan from the Harvard Medical School professor. What is wrong with the guy that he is compelled to do this? he asks. Seen in the best possible way, he is totally convinced that he is the savior of mankind developing the fountain of youth. But you dont have to hype to do that. Just let the facts play out. Even his friends call him out for how he talks about his science. David is a good friend, Austad says, but I do think hes been guilty of making excessive claims.

Despite the resveratrol fiasco, Sinclair hasnt shied away from making other grandiose promises. One of his more recent molecules of interest is called NMN. It is found in every living cell and boosts levels of something called NAD+, which regulates the mitochondria, or powerhouses, in all of our cells. NAD+ declines with ageunless, that is, scientists like Sinclair can find a way to increase it. Last year, he told Time magazine that NAD+ is the closest weve gotten to a fountain of youth.

If Sinclairs public comments push past the limits of what most scientists would say, it is also true that his accomplishments in the lab continue to push the limits of science itself. When I met with Sinclair, he told me he is gearing up to publish a paper about how his lab reversed aging in rodents. He described a series of experiments using gene therapy in which he and a group of scientists were able to restore vision in mice with glaucoma as well as in other mice who had their optic nerves (which cannot grow back after the newborn period) crushed. Sinclairs team had made a handful of old mice young again.

In light of the cutting-edge experiments and advances he is making in his lab, I was surprised that Sinclair also continues to study resveratrol. It seems so yesterday. When I asked about it, he assured me with a self-confident nod that he is still bullish on resveratrol. The 2013 paper, the one on his wall he believes vindicated him, didnt get the word out far and wide enough, he says. Thats why his lab did another experimentthis time deactivating a spot on the sirtuin protein in miceto show that resveratrol does, in fact, work. He tells me hes really looking forward to that study coming out to restore faith in resveratrol. And, it seems, perhaps to restore faith in Sinclair, too. When that one comes in, he says of the forthcoming paper, Im going to dropthe mike.

If Sinclairs public comments push past the limits of what most scientists would say, it is also true that his accomplishments in the lab continue to push the limits of science itself.

As Sinclair and I neared our destination in Worcester, I had my head down, furiously scribbling in my notebook, when I felt the car swerve abruptly to the right. I looked up to see Sinclair, visibly frustrated, struggling with the Teslas steering wheel. My car appears to have been set to Mad Max mode, he said in his pitch-perfect Australian accent. I promise not to get us killed. Then he added wryly, That would be ironic.

It would, indeed. After all, Sinclair is planning on being around for a lot longer than most people think they will. He convinced his dentist to fix some wear on his teeth, a procedure that she told him shed normally reserve only for teenagers. He dedicated his book to his great-great-grandchildren, whom he is very much looking forward to meeting.

To make it until then, he practices calorie restriction, eats a mostly vegetarian diet, and tries to avoid sugar and carbs. On weekends, he exercises at the gym and then sits in a hot sauna before plunging himself into an ice-cold pool, because temperature extremes also kick our cells survival instincts into action, he says. Sinclair tracks his biomarkers regularly and takes vitamin D, vitamin K2, and aspirin. And he takes three other substances each morning: resveratrol, NMN, and metformin, a diabetes drug currently being studied for its potential anti-aging effects. The problem, critics say, is that unlike cancer drugs, for instance, nearly anyone can buy something close to the NMN and resveratrol capsules Sinclair is downing at places like the local GNC, where theyre sold as supplements alongside multivitamins and protein powder.

Sinclair diligently points out that he is not a medical doctor; that he is not recommending anyone do what he does; and that there is no definitive evidence that any of it helps humans. Still, critics say that when a scientist such as Sinclair tells people what he is taking, it is nothing short of a celebrity endorsement, those caveats notwithstanding. In his defense, he told me he gets dozens of emails and messages every day from people asking him what theyor their petsshould be taking, and that he never makes recommendations. But its also hard to imagine people would write to ask him at all if he werent talking so publiclyand so oftenabout his daily regimen. I like David a lot. Were very good friends. However, I dont think that what hes doing is right, says Felipe Sierra, the director of the aging biology division at the National Institute of Aging. I dont think that people should try it on themselves. And if they do, they shouldnt publicize it. Researchers do have a responsibility toward the public, and we should be careful about what we tell the public.

Sinclair knows he ruffles feathers: At one point during our day together, I asked him where his family members get their pills from. He raised his eyebrows at me and then said in a Big-Brother-might-be-listening kind of whisper that we were in territory that could get me called into the office, and it wouldnt be the first time. Still, he says he is prepared to deal with the consequences of being honest.

Whats more, Sinclair says he has nothing to do with the supplement industry, a claim that is mostly true. All of the companies he has started are working on creating FDA-approved drugs, not supplements. True, years ago he did work as a paid adviser to a resveratrol supplement company, Shaklee, though Sinclair says he cut off that relationship when the company started using his name for marketing.

Even if Sinclair isnt directly profiting when people buy supplements after hearing him speak, he may still be benefiting financially from talking about what he takes. Think about what the optics would be if someone says, Ive got this great potential therapeutic intervention, and then says theyre not taking it. Suddenly you are putting up red flags about your own science, Olshansky, the Illinois professor, says. So I can see why somebody who has a financial interest in a molecule would take it and brag about it. If it helps them get more money to do research, that may be one of the reasons they do it. Sierra, for his part, admits that as much as he dislikes when Sinclair shares what he is taking, it is probably good for commercial purposes.

Whether or not his personal habits have helped Sinclairs bottom line, theres no doubt hes raised a ton of funding and used it to start a slew of companies. Seven of them fall under the umbrella of Life Biosciences, a Boston holding company he cofounded with Australian investor Tristan Edwards with the goal of building clinical-stage biotech companies by harnessing the best science in the aging field. Edwards had been interested in the longevity space and searched for a scientist to work with. He had a call with Sinclair and was so convinced by what he heard that before he got off the phone, he had already booked a flight to Boston. The firm raised $25 million while in stealth mode in 2017 and has since raised $500 million more.

Another company, MetroBiotech (which falls under the holding company EdenRoc Sciences), is pursuing drugs inspired by the NAD+ booster NMN. Thats the one we were on our way to visit when Sinclairs Tesla tried to kill us. Upon our arrival, two men looking slightly disheveled and both wearing Hawaiian shirts greeted us; these were the organic chemists tasked with developing molecules that may one day become an FDA-approved drug. As they took me back to their lab, I noticed the paunch on one of them, the wrinkles on the other, and the fact that what little hair either of them had left on their heads was somewhere between gray and white. I lowered my voice and asked, So are you guys, you know, taking the stuff?

Of course not. We are scientists! one of them exclaimed, looking at me like I was the mad scientist in the room.

It doesnt take a PhD to know that the fact that two guys who arent taking NMN look old proves absolutely nothing. But it did make me feel a little more hopeful to learn that they were not. And the funny thing is that later in the day, when I asked Sinclair why he takes unapproved drugs knowing that there could be risks (and how much it pisses people off), he said the very same thing: I take them because I am a scientist.

Then, in total deadpan, he gave me another reason.

And because I would like to outlive my enemies.

David Sinclair with his wife, Sandra Luikenhuis, at the Time 100 party after the publication named him one of the worlds most influential people in 2014. / Getty Images

Sinclair and I were supposed to be at the gym at 5 p.m. to meet up with his 12-year-old son, Ben, and his about-to-be-80-year-old father. Because we were running late, he asked his wife to send his gym clothes with his dad. When we arrived, Sinclair came out of the locker room in his dress shoes. His wife, despite taking NMN herself, had forgotten to send his sneakers. Luckily, the trainer had an extra pair, and the Sinclair family got down to business.

First up were dead lifts. Ben had a go and did pretty well for a kid his age. Then Sinclair went. He started to wince midway into the second set but made it through. Finally, his father had his turn, dead-lifting 95 and then 115 pounds like it was nothing. The trainer told me most of his 80-year-old clients are working on maintaining their balance and lifting themselves out of chairs. Sinclairs dad is killing it in the gym. Well, I suppose the only thing this proves is how useless I am, Sinclair told me, frowning.

Of course, he is hoping it means something else. His father has been taking NMN for two years, and since starting, Sinclair said, it has changed his life, his attitude, and his energy levels. It has returned to him his joie de vivre.

When I asked Sinclairs dad directly how the pills are going for him, I realized that Sinclair definitely did not get his salesmanship skills from his father. Cant tell, he told me flatly, with a shrug. But all my friends are dying or going downhill and Im not.

Not only are Sinclairs dad and wife taking NMN, but so are his two dogs. His younger brother grew gray hairs and developed wrinkles before he accused Sinclair of using him as a negative control in his little family experiment. Sinclair admits the thought did cross his mind, but blood is thicker than science, and now his brother is on the regimen, too. Even several of his graduate students are taking some of the pills. When the postmenopausal mother of one of those grad students also began taking it, she started menstruating again. (Perhaps unsurprisingly, Sinclair has a fertility company, too.)

There was one person who never got the chance to take NMN, however, and it seems to haunt Sinclair. His mother was diagnosed with lung cancer at age 50 and had a lung removed. She managed to live another 20 years with one lung, which Sinclair says he would like to think had something to do with the fact that she took resveratrol. At the end of her life, when she took a turn for the worse, Sinclair packed some NMN in his suitcase and boarded a flight to Australia. When he got there, she started doing so much better that the doctors took her off her respirator, and she never took the NMN. She died unexpectedly 12 hours later. I thought the NMN would save her, he admits. Wouldnt anybody do whatever they can to try to save their mother?

As their workout wore on, Sinclairs son Ben had something he wanted to tell me. He wanted me to know that he would like to continue his fathers work if he ever dies. I was distracted from the tenderness of this statement by the presence of a single preposition.

If? I asked.

He may never die, he said.

I shrugged and smiled, but inside I was thinking that if he isnt joking, someone is in for a real shocker. Earlier in the day, Sinclair told me he was such a straight-talker that he had ruined the illusion of Santa Claus for his childrenand yet here his son could be thinking his father might never die. Such is life in the Sinclair household.

Still, not everyone in the family wants to see people live forever. Sinclairs oldest daughter doesnt agree with his work and has zero qualms about letting her dad know it. She has asked him why, when previous generations have screwed up this planet so royally, he thinks its a good idea to have the people who did the damage hang around any longer. She is not the only one. Emory University bioethicist Paul Root Wolpe, for instance, has called the longevity field a narcissistic quest and points out that generational shifts are necessary for innovation, progress, and social change.

As if in response, Sinclairs book has an end section in which he delves into many ways to fix the world he wants to create. There is, he argues, a solution to everything in a reality where people live to 150overpopulation, inequality, natural-resource limitationsif you are as hopeful as he is. Just as I was finishing up this piece, in fact, scientists published a study linking optimism to longevitymeaning Sinclair could stand to add even more years to his life. Indeed, if I squint hard enough, I can practically see him growing younger before my very eyes.

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Has Harvard's David Sinclair Found the Fountain of Youth?

Google Engineers ‘Mutate’ AI to Make It Evolve Systems Faster Than We Can Code Them – ScienceAlert

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention.

For years, engineers at Google have been working on a freakishly smart machine learning system known as theAutoML system(or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made.

Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding.

The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

"It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks," write the researchers in their pre-print paper. "We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space."

The original AutoML system is intended to make it easier for apps to leverage machine learning, and already includes plenty of automated features itself, but AutoML-Zero takes the required amount of human input way down.

Using a simple three-step process - setup, predict and learn - it can be thought of as machine learning from scratch.

The system starts off with a selection of 100 algorithms made by randomly combining simple mathematical operations. A sophisticated trial-and-error process then identifies the best performers, which are retained - with some tweaks - for another round of trials. In other words, the neural network is mutating as it goes.

When new code is produced, it's tested on AI tasks - like spotting the difference between a picture of a truck and a picture of a dog - and the best-performing algorithms are then kept for future iteration. Like survival of the fittest.

And it's fast too: the researchers reckon up to 10,000 possible algorithms can be searched through per second per processor (the more computer processors available for the task, the quicker it can work).

Eventually, this should see artificial intelligence systems become more widely used, and easier to access for programmers with no AI expertise. It might even help us eradicate human bias from AI, because humans are barely involved.

Work to improve AutoML-Zero continues, with the hope that it'll eventually be able to spit out algorithms that mere human programmers would never have thought of. Right now it's only capable of producing simple AI systems, but the researchers think the complexity can be scaled up rather rapidly.

"While most people were taking baby steps, [the researchers] took a giant leap into the unknown," computer scientist Risto Miikkulainen from the University of Texas, Austin, who was not involved in the work, told Edd Gent at Science. "This is one of those papers that could launch a lot of future research."

The research paper has yet to be published in a peer-reviewed journal, but can be viewed online at arXiv.org.

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Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them - ScienceAlert

Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists – Newsweek

Computer scientists working for a high-tech division of Google are testing how machine learning algorithms can be created from scratch, then evolve naturally, based on simple math.

Experts behind Google's AutoML suite of artificial intelligence tools have now showcased fresh research which suggests the existing software could potentially be updated to "automatically discover" completely unknown algorithms while also reducing human bias during the data input process.

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According to ScienceMag, the software, known as AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.

Machine learning tools are "trained" to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience.

But researchers say this comes with drawbacks that AutoML-Zero aims to fix. Namely, the introduction of bias.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," their team's paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

The analysis, which was published last month on arXiv, is titled "Evolving Machine Learning Algorithms From Scratch" and is credited to a team working for Google Brain division.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

As noted by ScienceMag, AutoML-Zero is designed to create a population of 100 "candidate algorithms" by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then "evolve" by randomly changing their code.

The resultswhich will be variants of the most successful algorithmsthen get added to the general population, as older and less successful algorithms get left behind, and the process continues to repeat. The network grows significantly, in turn giving the system more natural algorithms to work with.

Haran Jackson, the chief technology officer (CTO) at Techspert, who has a PhD in Computing from the University of Cambridge, told Newsweek that AutoML tools are typically used to "identify and extract" the most useful features from datasetsand this approach is a welcome development.

"As exciting as AutoML is, it is restricted to finding top-performing algorithms out of the, admittedly large, assortment of algorithms that we already know of," he said.

"There is a sense amongst many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised.

"This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms."

Jackson, too, said the approach taken was similar to the facts of evolution first proposed by Charles Darwin, noting how the Google team was able to induce "mutations" into the set of algorithms.

"The mutated algorithms that did a better job of solving real-world problems were kept alive, with the poorly-performing ones being discarded," he elaborated.

"This was done repeatedly, until a set of high-performing algorithms was found. One intriguing aspect of the study is that this process 'rediscovered' some of the neural network algorithms that we already know and use. It's extremely exciting to see if it can turn up any algorithms that we haven't even thought of yet, the impact of which to our daily lives may be enormous." Google has been contacted for comment.

The development of AutoML was previously praised by Alphabet's CEO Sundar Pichai, who said it had been used to improve an algorithm that could detect the spread of breast cancer to adjacent lymph nodes. "It's inspiring to see how AI is starting to bear fruit," he wrote in a 2018 blog post.

The Google Brain team members who collaborated on the paper said the concepts in the most recent research were a solid starting point, but stressed that the project is far from over.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

Walsh told Newsweek: "The developers of AutoML-Zero believe they have produced a system that has the ability to output algorithms human developers may never have thought of.

"According to the developers, due to its lack of human intervention AutoML-Zero has the potential to produce algorithms that are more free from human biases. This theoretically could result in cutting-edge algorithms that businesses could rely on to improve their efficiency.

"However, it is worth bearing in mind that for the time being the AI is still proof of concept and it will be some time before it is able to output the complex kinds of algorithms currently in use. On the other hand, the research [demonstrates how] the future of AI may be algorithms produced by other machines."

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Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists - Newsweek

Teslas acquisition of DeepScale starts to pay off with new IP in machine learning – Electrek

Teslas acquisition of machine-learning startup DeepScale is starting to pay off, with the team hired through the acquisition starting to deliver new IP for the automaker.

Late last year, it was revealed that Tesla acquired DeepScale, a Bay Area-based startup that focuses on Deep Neural Network (DNN) for self-driving vehicles, for an undisclosed amount.

They specialized in computing power-efficient deep learning systems, which is also an area of focus for Tesla, who decided to design its own computer chip to power its self-driving software.

There was speculation that Tesla acquired the small startup team in order to accelerate its machine learning development.

Now we are seeing some of that teams work, thanks to a new patent application.

Just days after Tesla acquired the startup in October 2019, the automaker applied for a new patent with three members of DeepScale listed as inventors: Matthew Cooper, Paras Jain, and Harsimran Singh Sidhu.

The patent application called Systems and Methods for Training Machine Models with Augmented Data was published yesterday.

Tesla writes about it in the application:

Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.

The system that the DeepScale team, now working under Tesla, is trying to patent here is related to training a neural net using data from several different sensors observing scenes, like the eight cameras in Teslas Autopilot sensor array.

They write about the difficulties of such a situation in the patent application:

In typical machine learning applications, data may be augmented in various ways to avoid overfitting the model to the characteristics of the capture equipment used to obtain the training data. For example, in typical sets of images used for training computer models, the images may represent objects captured with many different capture environments having varying sensor characteristics with respect to the objects being captured. For example, such images may be captured by various sensor characteristics, such as various scales (e.g., significantly different distances within the image), with various focal lengths, by various lens types, with various pre- or post-processing, different software environments, sensor array hardware, and so forth. These sensors may also differ with respect to different extrinsic parameters, such as the position and orientation of the imaging sensors with respect to the environment as the image is captured. All of these different types of sensor characteristics can cause the captured images to present differently and variously throughout the different images in the image set and make it more difficult to properly train a computer model.

Here they summarize their solution to the problem:

One embodiment is a method for training a set of parameters of a predictive computer model. This embodiment may include: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

An additional embodiment may include a system having one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

Another embodiment may include a non-transitory computer-readable medium having instructions for execution by a processor, the instructions when executed by the processor causing the processor to: identify a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identify a training output for the image; for one or more images in the set of images, generate an augmented image for a set of augmented images by: generate an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associate the augmented training image with the training output of the image; train the computer model to learn to predict the training output based on an image training set including the images and the set of augmented images.

As we previously reported, Tesla is going through a significant foundational rewrite in the Tesla Autopilot. As part of the rewrite, CEO Elon Musk says that the neural net is absorbing more and more of the problem.

It will also include a more in-depth labeling system.

Musk described 3D labeling as a game-changer:

Its where the car goes into a scene with eight cameras, and kind of paint a path, and then you can label that path in 3D.

This new way to train machine learning systems with multiple cameras, like Teslas Autopilot, with augmented data could be part of this new Autopilot update.

Here are some drawings from the patent application:

Heres Teslas patent application in full:

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Teslas acquisition of DeepScale starts to pay off with new IP in machine learning - Electrek

New AI improves itself through Darwinian-style evolution – Big Think

Machine learning has fundamentally changed how we engage with technology. Today, it's able to curate social media feeds, recognize complex images, drive cars down the interstate, and even diagnose medical conditions, to name a few tasks.

But while machine learning technology can do some things automatically, it still requires a lot of input from human engineers to set it up, and point it in the right direction. Inevitably, that means human biases and limitations are baked into the technology.

So, what if scientists could minimize their influence on the process by creating a system that generates its own machine-learning algorithms? Could it discover new solutions that humans never considered?

To answer these questions, a team of computer scientists at Google developed a project called AutoML-Zero, which is described in a preprint paper published on arXiv.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," the paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

Automatic machine learning (AutoML) is a fast-growing area of deep learning. In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems. Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.

AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms "from scratch," as the paper states. Then, it selects the best ones, and mutates them through a process that's similar to Darwinian evolution.

AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image. The performance of these algorithms is compared to hand-designed algorithms. AutoML-Zero then selects the top-performing algorithm to be the "parent."

"This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed," the paper states.

The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

If computer scientists can scale up this kind of automated machine-learning to complete more complex tasks, it could usher in a new era of machine learning where systems are designed by machines instead of humans. This would likely make it much cheaper to reap the benefits of deep learning, while also leading to novel solutions to real-world problems.

Still, the recent paper was a small-scale proof of concept, and the researchers note that much more research is needed.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

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New AI improves itself through Darwinian-style evolution - Big Think

Model quantifies the impact of quarantine measures on Covid-19’s spread – MIT News

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

Research Team Uses Machine Learning to Track COVID-19 Spread in Communities and Predict Patient Outcomes – The Ritz Herald

The COVID-19 pandemic is raising critical questions regarding the dynamics of the disease, its risk factors, and the best approach to address it in healthcare systems. MIT Sloan School of Management Prof. Dimitris Bertsimas and nearly two dozen doctoral students are using machine learning and optimization to find answers. Their effort is summarized in the COVIDanalytics platform where their models are generating accurate real-time insight into the pandemic. The group is focusing on four main directions; predicting disease progression, optimizing resource allocation, uncovering clinically important insights, and assisting in the development of COVID-19 testing.

The backbone for each of these analytics projects is data, which weve extracted from public registries, clinical Electronic Health Records, as well as over 120 research papers that we compiled in a new database. Were testing our models against incoming data to determine if it makes good predictions, and we continue to add new data and use machine-learning to make the models more accurate, says Bertsimas.

The first project addresses dilemmas at the front line, such as the need for more supplies and equipment. Protective gear must go to healthcare workers and ventilators to critically ill patients. The researchers developed an epidemiological model to track the progression of COVID-19 in a community, so hospitals can predict surges and determine how to allocate resources.

The team quickly realized that the dynamics of the pandemic differ from one state to another, creating opportunities to mitigate shortages by pooling some of the ventilator supply across states. Thus, they employed optimization to see how ventilators could be shared among the states and created an interactive application that can help both the federal and state governments.

Different regions will hit their peak number of cases at different times, meaning their need for supplies will fluctuate over the course of weeks. This model could be helpful in shaping future public policy, notes Bertsimas.

Recently, the researchers connected with long-time collaborators at Hartford HealthCare to deploy the model, helping the network of seven campuses to assess their needs. Coupling county level data with the patient records, they are rethinking the way resources are allocated across the different clinics to minimize potential shortages.

The third project focuses on building a mortality and disease progression calculator to predict whether someone has the virus, and whether they need hospitalization or even more intensive care. He points out that current advice for patients is at best based on age, and perhaps some symptoms. As data about individual patients is limited, their model uses machine learning based on symptoms, demographics, comorbidities, lab test results as well as a simulation model to generate patient data. Data from new studies is continually added to the model as it becomes available.

We started with data published in Wuhan, Italy, and the U.S., including infection and death rate as well as data coming from patients in the ICU and the effects of social isolation. We enriched them with clinical records from a major hospital in Lombardy which was severely impacted by the spread of the virus. Through that process, we created a new model that is quite accurate. Its power comes from its ability to learn from the data, says Bertsimas.

By probing the severity of the disease in a patient, it can actually guide clinicians in congested areas in a much better way, says Bertsimas.

Their fourth project involves creating a convenient test for COVID-19. Using data from about 100 samples from Morocco, the group is using machine-learning to augment a test previously designed at the Mohammed VI Polytechnic University to come up with more precise results. The model can accurately detect the virus in patients around 90% of the time, while false positives are low.

The team is currently working on expanding the epidemiological model to a global scale, creating more accurate and informed clinical risk calculators, and identifying potential ways that would allow us to go back to normality.

We have released all our source code and made the public database available for other people too. We will continue to do our own analysis, but if other people have better ideas, we welcome them, says Bertsimas.

Continued here:
Research Team Uses Machine Learning to Track COVID-19 Spread in Communities and Predict Patient Outcomes - The Ritz Herald

Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more – OnMSFT

Welcome back to our Windows 10 news recap, where we go over the top stories of the past week in the world of Microsofts flagship operating system.

Microsoft to introduce PowerToys launcher for Windows 10 in May

A new report suggests that a new update for PowerToys is being prepared that includes a Mac OS style Spotlight launcher, making it easier find apps and files on a Windows 10 PC.

concept design for PowerToys Launcher UX

Microsoft starts sending invites for first Halo 2 Anniversary beta on PC

Invites for the Halo 2 Anniversary beta on PC have started to be sent out this week. Members of the Halo Insider program who have opted into PC flighting will receive an email with the invite.

Microsoft is using machine learning to identify security bugs during software development

In order to help Microsoft identify security bugs and resolve them before public release of software, the company is employing machine learning to find security bugs.

Thats it for this week. Well be back next week with more Windows 10 news.

Originally posted here:
Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more - OnMSFT

Create Symbiotic Relationships with AI in Business – ReadWrite

Knowingly or unknowingly we are all using artificial intelligence or AI. There is a combination of always-on devices, cloud and edge computing, and APIs in our everyday lives and business practices bringing AI into practice. Here is how to create symbiotic relationships with AI in business.

Even though the relationship between humans and machines is growing ever closer, its much too early to describe many of these collaborations as symbiotic.

When humans have specific types of problems, weve built and trained machines to solve those problems.

Examples include machine learning or ML. The ML algorithms that can identify cancer in brain images. The algorithms can also determine the best placements or designs for online ads, and there are deep learning systems that can predict customer churn in business.

At the moment, we can only imagine how much more productive we will become as we form symbiotic relationships with AI. Routine tasks that currently take hours or days could be abbreviated to 10 or 15 minutes with the aid of a digital partner.

From simple exercises like finding a new restaurant to more expert tasks such as cancer detection, we will increasingly rely on machines for everyday tasks. Dependence on machines might begin as a second pair of eyes or a second opinion, but our commitment to machines (and AI) will evolve into full-on digital collaborators.

Machine learning could bring about a revolution in how we solve problems to which the principle of optimal stopping applies.

Research in mathematics and computer science regarding these problems has shown that the optimal time to stop searching and make a decision is after37% of the time has been spent, options have been reviewed, and parking spaces have been passed.

Examples of these sorts of traditions problems include hiring the right person, making the right amount of R&D investment, and buying or selling a home. Humans tend to stop searching and considering data at about 31% well before they could have found the best option.

Forming symbiotic relationships with machines will free up time for us to focus on honing soft skills such as empathy, management, and strategy. It is not unreasonable to conclude that this symbiotic relationship will even present a new factor in the simple ability to enjoy life outside of work.

Very soon, AI could help us review enough options to find the right homebuyer, apartment tenant, job applicant, and perhaps even the right spouse.

For businesses and organizations with knowledge work as their output employees will benefit in several ways by applying machine learning to their advantage. Employees will use applications that cut across a variety of industries.

Some industry-agnostic roles such as a project manager will be able to offload routine tasks.

Tech will benefit substantially. Similar to how content creators benefit from writing agents such as Grammarly, software developers will benefit from a pair programming agent. The agent will suggest not only the right code syntax, but also the most appropriate framework, library, or API.

These agents will also have the opportunity to improve code quality and user experience drastically.

For industries like construction, AI could take advantage of the increased digitization of blueprints. AI will automate tasks that are routine but critical as project estimation. Depending on the size of the project, a human estimator can take up to four weeks to estimate a project.

Effortlessly, a digital agent could determine the materials needed for the project and set the number of workers necessary to staff the project.

More dramatic still, the AI digital agent could be connected to a supply store and incorporate real-time pricing into the final quote.

Medicine is another prime exampleof an industry ripe for disruption through human-AI symbiosis.

Pharmaceutical companies are leveraging machine learning to determine the optimal levels of research and development, using factors such as projected market size, revenue, and lifetime value of potential drugs.

Many doctors and hospitals have begun to incorporate AI recommendations into their processes. Increasing successes are seen, with 35% of doctors in a 2019 survey stating they use AI in their practices.

Some approaches in medicine have leveraged AI to provide potential options to doctors. Other choices analyze a doctors recommendation to predict the probability of success.

The dynamic symbiotic relationship between doctors and AI will also likely alter how malpractice riskis assessed for insurance.

As AI becomesmore commonplace in healthcareand is proven to improve outcomes for patients and decrease costs for hospitals, malpractice insurance will evolve to see AI as a way to reduce overall risk.

Similarly, doctors and hospitals that invest in AI solutions will see an improved return on investment in the form of lower insurance costs, improved outcomes, and increased efficiency.

Organizations that want to embrace the advances in AI and ML to produce symbiotic relationships between machines and themselves can take these steps.

The first step is to assess how artificial intelligence stands to impact your business as well as your industry and value chain. Examine whether you can add AI to your services.

Will AI change your product entirely, or can AI open new possibilities for entirely new products and services?

Once you complete your assessment and identify your options, break down your potential financial value to the organization. The assessment will uncover both potential risks you could incur and opportunities for new revenue streams you could open once you achieve AI-human symbiosis.

Every organization needs to learn where its data is stored and used. Proactively make this data available across the organization for experimentation, proofs of concepts, and other innovation projects.

Gain a firm understanding of what data you have and who owns it and share the information across the organization safely and democratically. The open network and feeling you are creating with this action are crucial to enabling machines to work for you, and sowing the seeds of innovation.

Assess your workforce to determine the roles that will most likely benefit from AI and machine learning solutions. The assessments can be divided into varying styles across individual employees or teams. These assessments include:

Data-driven thinkers versus big-picture focus thinkers.

Strengths in strategy versus problem-solving strengths.

Skill sets in software development versus the risk assessment skill set.

Is the talent expertise contained in surgery versus the expertise in research and development?

Machines are forging new opportunities for human work throughout the value chain as humans and machines collaborate to create more meaningful human jobs.

An organization must align its approach to building symbiotic relationships with its overarching purpose and that begins with leadership.

Leaders must excite their workforces about the ultimate goal of integrating AI, provide a clear vision for the organizations goals, and assure their workers that machines will enhance and alter (but not replace) their roles.

Its important to create near and long-term plans and then share those timelines across the organization, and connect those benchmarks to your greater purpose.

Organizations wont be able to take advantage of the value of these symbiotic relationships without carefully appraising the opportunities and risks.

Businesses must get their data houses in order and encourage innovation that enhances their talent and their organizations purpose. Only then will humans use AI to its full potential.

Image Credit: franck-v, Unsplash

Daniel Williams is a principal with Pariveda Solutions, specializing in digital strategy, implementation, and analytics. With B.S. and M.S. degrees in Computer Science and Technology Management, he has become an expert in digital transformation and AI/ML.

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Create Symbiotic Relationships with AI in Business - ReadWrite