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#437209 A Renaissance of Genomics and Drugs Is ...

Posted on June 26, 2020 by Android

The causes of aging are extremely complex and unclear. But with longevity clinical trials increasing, more answers—and questions—are emerging than ever before.

With the dramatic demonetization of genome reading and editing over the past decade, and Big Pharma, startups, and the FDA starting to face aging as a disease, we are starting to turn those answers into practical ways to extend our healthspan.

In this article, I’ll explore how genome sequencing and editing, along with new classes of anti-aging drugs, are augmenting our biology to further extend our healthy lives.

Genome Sequencing and Editing
Your genome is the software that runs your body. A sequence of 3.2 billion letters makes you “you.” These base pairs of A’s, T’s, C’s, and G’s determine your hair color, your height, your personality, your propensity for disease, your lifespan, and so on.

Until recently, it’s been very difficult to rapidly and cheaply “read” these letters—and even more difficult to understand what they mean. Since 2001, the cost to sequence a whole human genome has plummeted exponentially, outpacing Moore’s Law threefold. From an initial cost of $3.7 billion, it dropped to $10 million in 2006, and to $1,500 in 2015.

Today, the cost of genome sequencing has dropped below $600, and according to Illumina, the world’s leading sequencing company, the process will soon cost about $100 and take about an hour to complete.

This represents one of the most powerful and transformative technology revolutions in healthcare. When we understand your genome, we’ll be able to understand how to optimize “you.”

We’ll know the perfect foods, the perfect drugs, the perfect exercise regimen, and the perfect supplements, just for you.
We’ll understand what microbiome types, or gut flora, are ideal for you (more on this in a later article).
We’ll accurately predict how specific sedatives and medicines will impact you.
We’ll learn which diseases and illnesses you’re most likely to develop and, more importantly, how to best prevent them from developing in the first place (rather than trying to cure them after the fact).

CRISPR Gene Editing
In addition to reading the human genome, scientists can now edit a genome using a naturally occurring biological system discovered in 1987 called CRISPR/Cas9.

Short for Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9, the editing system was adapted from a naturally-occurring defense system found in bacteria.

Here’s how it works. The bacteria capture snippets of DNA from invading viruses (or bacteriophage) and use them to create DNA segments known as CRISPR arrays. The CRISPR arrays allow the bacteria to “remember” the viruses (or closely related ones), and defend against future invasions. If the viruses attack again, the bacteria produce RNA segments from the CRISPR arrays to target the viruses’ DNA. The bacteria then use Cas9 to cut the DNA apart, which disables the virus.

Most importantly, CRISPR is cheap, quick, easy to use, and more accurate than all previous gene editing methods. As a result, CRISPR/Cas9 has swept through labs around the world as the way to edit a genome. A short search in the literature will show an exponential rise in the number of CRISPR-related publications and patents.

2018: Filled With CRISPR Breakthroughs
Early results are impressive. Researchers have used CRISPR to genetically engineer cocaine resistance into mice, reverse the gene defect causing Duchenne muscular dystrophy (DMD) in dogs, and reduce genetic deafness in mice.

Already this year, CRISPR-edited immune cells have been shown to successfully kill cancer cells in human patients. Researchers have discovered ways to activate CRISPR with light and use the gene-editing technology to better understand Alzheimer’s disease progression.

With great power comes great responsibility, and the opportunity for moral and ethical dilemmas. In 2015, Chinese scientists sparked global controversy when they first edited human embryo cells in the lab with the goal of modifying genes that would make the child resistant to smallpox, HIV, and cholera. Three years later, in November 2018, researcher He Jiankui informed the world that the first set of CRISPR-engineered female twins had been delivered.

To accomplish his goal, Jiankui deleted a region of a receptor on the surface of white blood cells known as CCR5, introducing a rare, natural genetic variation that makes it more difficult for HIV to infect its favorite target, white blood cells. Because Jiankui forged ethical review documents and misled doctors in the process, he was sentenced to three years in prison and fined $429,000 last December.

Coupled with significant ethical conversations necessary for progress, CRISPR will soon provide us the tools to eliminate diseases, create hardier offspring, produce new environmentally resistant crops, and even wipe out pathogens.

Senolytics, Nutraceuticals, and Pharmaceuticals
Over the arc of your life, the cells in your body divide until they reach what is known as the Hayflick limit, or the number of times a normal human cell population will divide before cell division stops, which is typically about 50 divisions.

What normally follows next is programmed cell death or destruction by the immune system. A very small fraction of cells, however, become senescent cells and evade this fate to linger indefinitely. These lingering cells secrete a potent mix of molecules that triggers chronic inflammation, damages the surrounding tissue structures, and changes the behavior of nearby cells for the worse. Senescent cells appear to be one of the root causes of aging, causing everything from fibrosis and blood vessel calcification to localized inflammatory conditions such as osteoarthritis to diminished lung function.

Fortunately, both the scientific and entrepreneurial communities have begun to work on senolytic therapies, moving the technology for selectively destroying senescent cells out of the laboratory and into a half-dozen startup companies.

Prominent companies in the field include the following:

Unity Biotechnology is developing senolytic medicines to selectively eliminate senescent cells with an initial focus on delivering localized therapy in osteoarthritis, ophthalmology, and pulmonary disease.

Oisin Biotechnologies is pioneering a programmable gene therapy that can destroy cells based on their internal biochemistry.

SIWA Therapeutics is working on an immunotherapy approach to the problem of senescent cells.

In recent years, researchers have identified or designed a handful of senolytic compounds that can curb aging by regulating senescent cells. Two of these drugs that have gained mainstay research traction are rapamycin and metformin.

(1) Rapamycin

Originally extracted from bacteria found on Easter Island, rapamycin acts on the m-TOR (mechanistic target of rapamycin) pathway to selectively block a key protein that facilitates cell division. Currently, rapamycin derivatives are widely used for immunosuppression in organ and bone marrow transplants. Research now suggests that use results in prolonged lifespan and enhanced cognitive and immune function.

PureTech Health subsidiary resTORbio (which went public in 2018) is working on a rapamycin-based drug intended to enhance immunity and reduce infection. Their clinical-stage RTB101 drug works by inhibiting part of the mTOR pathway.

Results of the drug’s recent clinical trial include decreased incidence of infection, improved influenza vaccination response, and a 30.6 percent decrease in respiratory tract infection.

Impressive, to say the least.

(2) Metformin

Metformin is a widely-used generic drug for mitigating liver sugar production in Type 2 diabetes patients. Researchers have found that metformin also reduces oxidative stress and inflammation, which otherwise increase as we age. There is strong evidence that metformin can augment cellular regeneration and dramatically mitigate cellular senescence by reducing both oxidative stress and inflammation.

Over 100 studies registered on ClinicalTrials.gov are currently following up on strong evidence of metformin’s protective effect against cancer.

(3) Nutraceuticals and NAD+

Beyond cellular senescence, certain critical nutrients and proteins tend to decline as a function of age. Nutraceuticals combat aging by supplementing and replenishing these declining nutrient levels.

NAD+ exists in every cell, participating in every process from DNA repair to creating the energy vital for cellular processes. It’s been shown that NAD+ levels decline as we age.

The Elysium Health Basis supplement aims to elevate NAD+ levels in the body to extend one’s lifespan. Elysium’s first clinical study reports that Basis increases NAD+ levels consistently by a sustained 40 percent.

Conclusion
These are just a taste of the tremendous momentum that longevity and aging technology has right now. As artificial intelligence and quantum computing transform how we decode our DNA and how we discover drugs, genetics and pharmaceuticals will become truly personalized.

The next article in this series will demonstrate how artificial intelligence is converging with genetics and pharmaceuticals to transform how we approach longevity, aging, and vitality.

We are edging closer toward a dramatically extended healthspan—where 100 is the new 60. What will you create, where will you explore, and how will you spend your time if you are able to add an additional 40 healthy years to your life?

Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”

If you’d like to learn more and consider joining our 2021 membership, apply here.

(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.

(Both A360 and Abundance-Digital are part of Singularity University—your participation opens you to a global community.)

This article originally appeared on diamandis.com. Read the original article here.

Image Credit: Arek Socha from Pixabay Continue reading →

Posted in Human Robots

#437103 How to Make Sense of Uncertainty in a ...

Posted on May 1, 2020 by Android

As the internet churns with information about Covid-19, about the virus that causes the disease, and about what we’re supposed to do to fight it, it can be difficult to see the forest for the trees. What can we realistically expect for the rest of 2020? And how do we even know what’s realistic?

Today, humanity’s primary, ideal goal is to eliminate the virus, SARS-CoV-2, and Covid-19. Our second-choice goal is to control virus transmission. Either way, we have three big aims: to save lives, to return to public life, and to keep the economy functioning.

To hit our second-choice goal—and maybe even our primary goal—countries are pursuing five major public health strategies. Note that many of these advances cross-fertilize: for example, advances in virus testing and antibody testing will drive data-based prevention efforts.

Five major public health strategies are underway to bring Covid-19 under control and to contain the spread of SARS-CoV-2.
These strategies arise from things we can control based on the things that we know at any given moment. But what about the things we can’t control and don’t yet know?

The biology of the virus and how it interacts with our bodies is what it is, so we should seek to understand it as thoroughly as possible. How long any immunity gained from prior infection lasts—and indeed whether people develop meaningful immunity at all after infection—are open questions urgently in need of greater clarity. Similarly, right now it’s important to focus on understanding rather than making assumptions about environmental factors like seasonality.

But the biggest question on everyone’s lips is, “When?” When will we see therapeutic progress against Covid-19? And when will life get “back to normal”? There are lots of models out there on the internet; which of those models are right? The simple answer is “none of them.” That’s right—it’s almost certain that every model you’ve seen is wrong in at least one detail, if not all of them. But modeling is meant to be a tool for deeper thinking, a way to run mental (and computational) experiments before—and while—taking action. As George E. P. Box famously wrote in 1976, “All models are wrong, but some are useful.”

Here, we’re seeking useful insights, as opposed to exact predictions, which is why we’re pulling back from quantitative details to get at the mindsets that will support agency and hope. To that end, I’ve been putting together timelines that I believe will yield useful expectations for the next year or two—and asking how optimistic I need to be in order to believe a particular timeline.

For a moderately optimistic scenario to be relevant, breakthroughs in science and technology come at paces expected based on previous efforts and assumptions that turn out to be basically correct; accessibility of those breakthroughs increases at a reasonable pace; regulation achieves its desired effects, without major surprises; and compliance with regulations is reasonably high.

In contrast, if I’m being highly optimistic, breakthroughs in science and technology and their accessibility come more quickly than they ever have before; regulation is evidence-based and successful in the first try or two; and compliance with those regulations is high and uniform. If I’m feeling not-so-optimistic, then I anticipate serious setbacks to breakthroughs and accessibility (with the overturning of many important assumptions), repeated failure of regulations to achieve their desired outcomes, and low compliance with those regulations.

The following scenarios outline the things that need to happen in the fight against Covid-19, when I expect to see them, and how confident I feel in those expectations. They focus on North America and Europe because there are data missing about China’s 2019 outbreak and other regions are still early in their outbreaks. Perhaps the most important thing to keep in mind throughout: We know more today than we did yesterday, but we still have much to learn. New knowledge derived from greater study and debate will almost certainly inspire ongoing course corrections.

As you dive into the scenarios below, practice these three mindset shifts. First, defeating Covid-19 will be a marathon, not a sprint. We shouldn’t expect life to look like 2019 for the next year or two—if ever. As Ed Yong wrote recently in The Atlantic, “There won’t be an obvious moment when everything is under control and regular life can safely resume.” Second, remember that you have important things to do for at least a year. And third, we are all in this together. There is no “us” and “them.” We must all be alert, responsive, generous, and strong throughout 2020 and 2021—and willing to throw away our assumptions when scientific evidence invalidates them.

The Middle Way: Moderate Optimism
Let’s start with the case in which I have the most confidence: moderate optimism.

This timeline considers milestones through late 2021, the earliest that I believe vaccines will become available. The “normal” timeline for developing a vaccine for diseases like seasonal flu is 18 months, which leads to my projection that we could potentially have vaccines as soon as 18 months from the first quarter of 2020. While Melinda Gates agrees with that projection, others (including AI) believe that 3 to 5 years is far more realistic, based on past vaccine development and the need to test safety and efficacy in humans. However, repurposing existing vaccines against other diseases—or piggybacking off clever synthetic platforms—could lead to vaccines being available sooner. I tried to balance these considerations for this moderately optimistic scenario. Either way, deploying vaccines at the end of 2021 is probably much later than you may have been led to believe by the hype engine. Again, if you take away only one message from this article, remember that the fight against Covid-19 is a marathon, not a sprint.

Here, I’ve visualized a moderately optimistic scenario as a baseline. Think of these timelines as living guides, as opposed to exact predictions. There are still many unknowns. More or less optimistic views (see below) and new information could shift these timelines forward or back and change the details of the strategies.
Based on current data, I expect that the first wave of Covid-19 cases (where we are now) will continue to subside in many areas, leading governments to ease restrictions in an effort to get people back to work. We’re already seeing movement in that direction, with a variety of benchmarks and changes at state and country levels around the world. But depending on the details of the changes, easing restrictions will probably cause a second wave of sickness (see Germany and Singapore), which should lead governments to reimpose at least some restrictions.

In tandem, therapeutic efforts will be transitioning from emergency treatments to treatments that have been approved based on safety and efficacy data in clinical trials. In a moderately optimistic scenario, assuming clinical trials currently underway yield at least a few positive results, this shift to mostly approved therapies could happen as early as the third or fourth quarter of this year and continue from there. One approval that should come rather quickly is for plasma therapies, in which the blood from people who have recovered from Covid-19 is used as a source of antibodies for people who are currently sick.

Companies around the world are working on both viral and antibody testing, focusing on speed, accuracy, reliability, and wide accessibility. While these tests are currently being run in hospitals and research laboratories, at-home testing is a critical component of the mass testing we’ll need to keep viral spread in check. These are needed to minimize the impact of asymptomatic cases, test the assumption that infection yields resistance to subsequent infection (and whether it lasts), and construct potential immunity passports if this assumption holds. Testing is also needed for contact tracing efforts to prevent further spread and get people back to public life. Finally, it’s crucial to our fundamental understanding of the biology of SARS-CoV-2 and Covid-19.

We need tests that are very reliable, both in the clinic and at home. So, don’t go buying any at-home test kits just yet, even if you find them online. Wait for reliable test kits and deeper understanding of how a test result translates to everyday realities. If we’re moderately optimistic, in-clinic testing will rapidly expand this quarter and/or next, with the possibility of broadly available, high-quality at-home sampling (and perhaps even analysis) thereafter.

Note that testing is not likely to be a “one-and-done” endeavor, as a person’s infection and immunity status change over time. Expect to be testing yourself—and your family—often as we move later into 2020.

Testing data are also going to inform distancing requirements at the country and local levels. In this scenario, restrictions—at some level of stringency—could persist at least through the end of 2020, as most countries are way behind the curve on testing (Iceland is an informative exception). Governments will likely continue to ask citizens to work from home if at all possible; to wear masks or face coverings in public; to employ heightened hygiene and social distancing in workplaces; and to restrict travel and social gatherings. So while it’s likely we’ll be eating in local restaurants again in 2020 in this scenario, at least for a little while, it’s not likely we’ll be heading to big concerts any time soon.

The Extremes: High and Low Optimism
How would high and low levels of optimism change our moderately optimistic timeline? The milestones are the same, but the time required to achieve them is shorter or longer, respectively. Quantifying these shifts is less important than acknowledging and incorporating a range of possibilities into our view. It pays to pay attention to our bias. Here are a few examples of reasonable possibilities that could shift the moderately optimistic timeline.

When vaccines become available
Vaccine repurposing could shorten the time for vaccines to become available; today, many vaccine candidates are in various stages of testing. On the other hand, difficulties in manufacture and distribution, or faster-than-expected mutation of SARS-CoV-2, could slow vaccine development. Given what we know now, I am not strongly concerned about either of these possibilities—drug companies are rapidly expanding their capabilities, and viral mutation isn’t an urgent concern at this time based on sequencing data—but they could happen.

At first, governments will likely supply vaccines to essential workers such as healthcare workers, but it is essential that vaccines become widely available around the world as quickly and as safely as possible. Overall, I suggest a dose of skepticism when reading highly optimistic claims about a vaccine (or multiple vaccines) being available in 2020. Remember, a vaccine is a knockout punch, not a first line of defense for an outbreak.

When testing hits its stride
While I am confident that testing is a critical component of our response to Covid-19, reliability is incredibly important to testing for SARS-CoV-2 and for immunity to the disease, particularly at home. For an individual, a false negative (being told you don’t have antibodies when you really do) could be just as bad as a false positive (being told you do have antibodies when you really don’t). Those errors are compounded when governments are trying to make evidence-based policies for social and physical distancing.

If you’re highly optimistic, high-quality testing will ramp up quickly as companies and scientists innovate rapidly by cleverly combining multiple test modalities, digital signals, and cutting-edge tech like CRISPR. Pop-up testing labs could also take some pressure off hospitals and clinics.

If things don’t go well, reliability issues could hinder testing, manufacturing bottlenecks could limit availability, and both could hamstring efforts to control spread and ease restrictions. And if it turns out that immunity to Covid-19 isn’t working the way we assumed, then we must revisit our assumptions about our path(s) back to public life, as well as our vaccine-development strategies.

How quickly safe and effective treatments appear
Drug development is known to be long, costly, and fraught with failure. It’s not uncommon to see hope in a drug spike early only to be dashed later on down the road. With that in mind, the number of treatments currently under investigation is astonishing, as is the speed through which they’re proceeding through testing. Breakthroughs in a therapeutic area—for example in treating the seriously ill or in reducing viral spread after an infection takes hold—could motivate changes in the focus of distancing regulations.

While speed will save lives, we cannot overlook the importance of knowing a treatment’s efficacy (does it work against Covid-19?) and safety (does it make you sick in a different, or worse, way?). Repurposing drugs that have already been tested for other diseases is speeding innovation here, as is artificial intelligence.

Remarkable collaborations among governments and companies, large and small, are driving innovation in therapeutics and devices such as ventilators for treating the sick.

Whether government policies are effective and responsive
Those of us who have experienced lockdown are eager for it to be over. Businesses, economists, and governments are also eager to relieve the terrible pressure that is being exerted on the global economy. However, lifting restrictions will almost certainly lead to a resurgence in sickness.

Here, the future is hard to model because there are many, many factors at play, and at play differently in different places—including the extent to which individuals actually comply with regulations.

Reliable testing—both in the clinic and at home—is crucial to designing and implementing restrictions, monitoring their effectiveness, and updating them; delays in reliable testing could seriously hamper this design cycle. Lack of trust in governments and/or companies could also suppress uptake. That said, systems are already in place for contact tracing in East Asia. Other governments could learn important lessons, but must also earn—and keep—their citizens’ trust.

Expect to see restrictions descend and then lift in response to changes in the number of Covid-19 cases and in the effectiveness of our prevention strategies. Also expect country-specific and perhaps even area-specific responses that differ from each other. The benefit of this approach? Governments around the world are running perhaps hundreds of real-time experiments and design cycles in balancing health and the economy, and we can learn from the results.

A Way Out
As Jeremy Farrar, head of the Wellcome Trust, told Science magazine, “Science is the exit strategy.” Some of our greatest technological assistance is coming from artificial intelligence, digital tools for collaboration, and advances in biotechnology.

Our exit strategy also needs to include empathy and future visioning—because in the midst of this crisis, we are breaking ground for a new, post-Covid future.

What do we want that future to look like? How will the hard choices we make now about data ethics impact the future of surveillance? Will we continue to embrace inclusiveness and mass collaboration? Perhaps most importantly, will we lay the foundation for successfully confronting future challenges? Whether we’re thinking about the next pandemic (and there will be others) or the cascade of catastrophes that climate change is bringing ever closer—it’s important to remember that we all have the power to become agents of that change.

Special thanks to Ola Kowalewski and Jason Dorrier for significant conversations.

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Posted in Human Robots

#436977 The Top 100 AI Startups Out There Now, ...

Posted on March 30, 2020 by Android

New drug therapies for a range of chronic diseases. Defenses against various cyber attacks. Technologies to make cities work smarter. Weather and wildfire forecasts that boost safety and reduce risk. And commercial efforts to monetize so-called deepfakes.

What do all these disparate efforts have in common? They’re some of the solutions that the world’s most promising artificial intelligence startups are pursuing.

Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.

About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.

It uses that algorithm-generated data from what it calls a company’s Mosaic score—pulling together information on market trends, money, and momentum—along with other details ranging from patent activity to the latest news analysis to identify the best of the best.

“Our final list of companies is a mix of startups at various stages of R&D and product commercialization,” said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.

About 10 companies on the list are among the world’s most valuable AI startups. For instance, there’s San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.

Image courtesy of CB Insights
Funding for AI in Healthcare
Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platform’s diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.

In fact, there are more than a dozen AI healthcare startups on this year’s AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.

One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. There’s even a chance AI could help fight the coronavirus pandemic.

There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.

And then there’s OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the company’s AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.

Keeping Cyber Networks Healthy
Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.

“I think this is an interesting field because it’s a bit of a cat and mouse game,” noted Varadharajanis. “As your cyber defenses get smarter, your cyber attacks get even smarter, and so it’s a constant game of who’s going to match the other in terms of tech capabilities.”

Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The company’s platform automates what’s called endpoint security, referring to laptops, phones, and other devices at the “end” of a centralized network.

Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the “edge” of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.

Deepfakes Get a Friendly Makeover
Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.

Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a user’s face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.

Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startup’s claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.

There’s also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.

AI Helps Make Smart Cities Smarter
Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least that’s the dream.

A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. It’s sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.

Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.

Some people might complain that weather forecasters don’t even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.

And those are just some of the highlights of what some of the world’s most promising AI startups are doing.

“You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards,” Varadharajanis said. “So a lot of creative ways in which companies are applying AI to solve different issues in different industries.”

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Posted in Human Robots

#436784 This Week’s Awesome Tech Stories From ...

Posted on March 1, 2020 by Android

COMPUTING
Inside the Race to Build the Best Quantum Computer on Earth
Gideon Lichfield | MIT Technology Review
“Regardless of whether you agree with Google’s position [on ‘quantum supremacy’] or IBM’s, the next goal is clear, Oliver says: to build a quantum computer that can do something useful. …The trouble is that it’s nearly impossible to predict what the first useful task will be, or how big a computer will be needed to perform it.”

FUTURE
We’re Not Prepared for the End of Moore’s Law
David Rotman | MIT Technology Review
“Quantum computing, carbon nanotube transistors, even spintronics, are enticing possibilities—but none are obvious replacements for the promise that Gordon Moore first saw in a simple integrated circuit. We need the research investments now to find out, though. Because one prediction is pretty much certain to come true: we’re always going to want more computing power.”

ROBOTICS
Flippy the Burger-Flipping Robot Is Changing the Face of Fast Food as We Know It
Luke Dormehl | Digital Trends
“Flippy is the result of the Miso team’s robotics expertise, coupled with that industry-specific knowledge. It’s a burger-flipping robot arm that’s equipped with both thermal and regular vision, which grills burgers to order while also advising human collaborators in the kitchen when they need to add cheese or prep buns for serving.”

BIOTECHNOLOGY
The Next Generation of Batteries Could Be Built by Viruses
Daniel Oberhaus | Wired
“[MIT bioengineering professor Angela Belcher has] made viruses that can work with over 150 different materials and demonstrated that her technique can be used to manufacture other materials like solar cells. Belcher’s dream of zipping around in a ‘virus-powered car’ still hasn’t come true, but after years of work she and her colleagues at MIT are on the cusp of taking the technology out of the lab and into the real world.”

SPACE
Biggest Cosmic Explosion Ever Detected Left Huge Dent in Space
Hannah Devlin | The Guardian
“The biggest cosmic explosion on record has been detected—an event so powerful that it punched a dent the size of 15 Milky Ways in the surrounding space. The eruption is thought to have originated at a supermassive black hole in the Ophiuchus galaxy cluster, which is about 390 million light years from Earth.”

SCIENCE FICTION
Star Trek’s Warp Speed Would Have Tragic Consequences
Cassidy Ward | SyFy
“The various crews of Trek‘s slate of television shows and movies can get from here to there without much fanfare. Seeking out new worlds and new civilizations is no more difficult than gassing up the car and packing a cooler full of junk food. And they don’t even need to do that! The replicators will crank out a bologna sandwich just like mom used to make. All that’s left is to go, but what happens then?”

Image Credit: sergio souza / Pexels Continue reading →

Posted in Human Robots

#436774 AI Is an Energy-Guzzler. We Need to ...

Posted on February 29, 2020 by Android

There is a saying that has emerged among the tech set in recent years: AI is the new electricity. The platitude refers to the disruptive power of artificial intelligence for driving advances in everything from transportation to predicting the weather.

Of course, the computers and data centers that support AI’s complex algorithms are very much dependent on electricity. While that may seem pretty obvious, it may be surprising to learn that AI can be extremely power-hungry, especially when it comes to training the models that enable machines to recognize your face in a photo or for Alexa to understand a voice command.

The scale of the problem is difficult to measure, but there have been some attempts to put hard numbers on the environmental cost.

For instance, one paper published on the open-access repository arXiv claimed that the carbon emissions for training a basic natural language processing (NLP) model—algorithms that process and understand language-based data—are equal to the CO2 produced by the average American lifestyle over two years. A more robust model required the equivalent of about 17 years’ worth of emissions.

The authors noted that about a decade ago, NLP models could do the job on a regular commercial laptop. Today, much more sophisticated AI models use specialized hardware like graphics processing units, or GPUs, a chip technology popularized by Nvidia for gaming that also proved capable of supporting computing tasks for AI.

OpenAI, a nonprofit research organization co-founded by tech prophet and profiteer Elon Musk, said that the computing power “used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time” since 2012. That’s about the time that GPUs started making their way into AI computing systems.

Getting Smarter About AI Chip Design
While GPUs from Nvidia remain the gold standard in AI hardware today, a number of startups have emerged to challenge the company’s industry dominance. Many are building chipsets designed to work more like the human brain, an area that’s been dubbed neuromorphic computing.

One of the leading companies in this arena is Graphcore, a UK startup that has raised more than $450 million and boasts a valuation of $1.95 billion. The company’s version of the GPU is an IPU, which stands for intelligence processing unit.

To build a computer brain more akin to a human one, the big brains at Graphcore are bypassing the precise but time-consuming number-crunching typical of a conventional microprocessor with one that’s content to get by on less precise arithmetic.

The results are essentially the same, but IPUs get the job done much quicker. Graphcore claimed it was able to train the popular BERT NLP model in just 56 hours, while tripling throughput and reducing latency by 20 percent.

An article in Bloomberg compared the approach to the “human brain shifting from calculating the exact GPS coordinates of a restaurant to just remembering its name and neighborhood.”

Graphcore’s hardware architecture also features more built-in memory processing, boosting efficiency because there’s less need to send as much data back and forth between chips. That’s similar to an approach adopted by a team of researchers in Italy that recently published a paper about a new computing circuit.

The novel circuit uses a device called a memristor that can execute a mathematical function known as a regression in just one operation. The approach attempts to mimic the human brain by processing data directly within the memory.

Daniele Ielmini at Politecnico di Milano, co-author of the Science Advances paper, told Singularity Hub that the main advantage of in-memory computing is the lack of any data movement, which is the main bottleneck of conventional digital computers, as well as the parallel processing of data that enables the intimate interactions among various currents and voltages within the memory array.

Ielmini explained that in-memory computing can have a “tremendous impact on energy efficiency of AI, as it can accelerate very advanced tasks by physical computation within the memory circuit.” He added that such “radical ideas” in hardware design will be needed in order to make a quantum leap in energy efficiency and time.

It’s Not Just a Hardware Problem
The emphasis on designing more efficient chip architecture might suggest that AI’s power hunger is essentially a hardware problem. That’s not the case, Ielmini noted.

“We believe that significant progress could be made by similar breakthroughs at the algorithm and dataset levels,” he said.

He’s not the only one.

One of the key research areas at Qualcomm’s AI research lab is energy efficiency. Max Welling, vice president of Qualcomm Technology R&D division, has written about the need for more power-efficient algorithms. He has gone so far as to suggest that AI algorithms will be measured by the amount of intelligence they provide per joule.

One emerging area being studied, Welling wrote, is the use of Bayesian deep learning for deep neural networks.

It’s all pretty heady stuff and easily the subject of a PhD thesis. The main thing to understand in this context is that Bayesian deep learning is another attempt to mimic how the brain processes information by introducing random values into the neural network. A benefit of Bayesian deep learning is that it compresses and quantifies data in order to reduce the complexity of a neural network. In turn, that reduces the number of “steps” required to recognize a dog as a dog—and the energy required to get the right result.

A team at Oak Ridge National Laboratory has previously demonstrated another way to improve AI energy efficiency by converting deep learning neural networks into what’s called a spiking neural network. The researchers spiked their deep spiking neural network (DSNN) by introducing a stochastic process that adds random values like Bayesian deep learning.

The DSNN actually imitates the way neurons interact with synapses, which send signals between brain cells. Individual “spikes” in the network indicate where to perform computations, lowering energy consumption because it disregards unnecessary computations.

The system is being used by cancer researchers to scan millions of clinical reports to unearth insights on causes and treatments of the disease.

Helping battle cancer is only one of many rewards we may reap from artificial intelligence in the future, as long as the benefits of those algorithms outweigh the costs of using them.

“Making AI more energy-efficient is an overarching objective that spans the fields of algorithms, systems, architecture, circuits, and devices,” Ielmini said.

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