London and San Francisco-based Big Health pitches itself as a provider of “evidence-based, non-drug” solutions to tackle various mental health disorders, including via the digital delivery of Cognitive Behavioral Therapy (CBT).
The startup’s first product is Sleepio, a digital sleep improvement program that claims to help users overcome poor sleep, and has already deployed to over 750,000 employees at companies including Comcast, LinkedIn, Boston Medical Center, and Henry Ford Health System.
Today Big Health, which is already backed by Index Ventures, is disclosing that it has raised $12 million (£9.15m) in new funding led by Octopus Ventures. Also participating are Kaiser Permanente Ventures, Index, Sean Duffy (CEO, Omada Health), and JamJar Investments (the UK-based investment fund of the innocent drinks founders). Noteworthy is that Esther Dyson, and Peter Read are also previous backers.
We first took an in-depth look into Big Health’s digital health delivery proposition back in 2014, rightly questioning whether or not CBT-related therapy could really be effective without face-to-face consultation. At the time, the startup’s co-founder Peter Hames was adamant that it could, citing early trials of its Sleepio programme.
A few years later and Big Health is presenting more evidence that this is the case, citing what it claims was the first placebo-group randomized controlled trial (RCT) for a digital sleep program that shows Sleepio comparable in effect to in-person CBT, with 76 percent of insomnia sufferers achieving healthy sleep (Espie, 2012).
Meanwhile, more recent studies appears to show Sleepio to be effective in helping anxiety sufferers reduce their symptoms (Pillai, 2015), and demonstrating significant improvements in sleep and productivity for users (Bostock, 2016).
To date, Big Health says there have been 14 published peer-reviewed papers, providing what it claims is the largest evidence base of any digital therapeutic addressing mental health.
Following yesterday’s news of the NHTSA’s investigation into a fatal crash involving a Tesla Model S, Mobileye, the Israeli technology company helping power the carmaker’s Autopilot feature, has sent a statement to TechCrunch regarding the incident.
“We have read the account of what happened in this case,” reads the text attributed to Dan Galves, Mobileye’s Chief Communications Officer. “Today’s collision avoidance technology, or Automatic Emergency Braking (AEB) is defined as rear-end collision avoidance, and is designed specifically for that.”
The statement strikes a decidedly different tone than yesterday’s Tesla announcement, which both mourned the loss of the driver (since identified as Ohio-based tech entrepreneur Joshua Brown) and reiterated the company’s safety measures, noting that, “Autopilot is getting better all the time, but it is not perfect and still requires the driver to remain alert.”
Galves, likewise, explains that the current generation of technology is not equipped to deal with the specific scenario that played out in the May 7th collision, though plans are in place to introduce it eventually. “This incident involved a laterally crossing vehicle, which current-generation AEB systems are not designed to actuate upon,” the statement explains. Mobileye systems will include Lateral Turn Across Path (LTAP) detection capabilities beginning in 2018, and the Euro NCAP safety ratings will include this beginning in 2020.”
The technology, it seems, has not yet been equipped to react to the specific case in which a vehicle turns across a lane. According to Tesla’s account from yesterday, “Neither Autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied.”
Mobileye was once again in the news this morning, when it was officially revealed that the company will be working with Intel to help bring BMW’s first fully self-driving car to market by 2021.
The market for augmented and virtual reality technology continues to heat up, and now one of the more promising startups making both AR hardware and software has raised a $50 million round to keep up the pace.
Meta, which makes an AR headset/glasses of the same name, as well as software to run on it, has raised $50 million in a Series B round of funding. The company plans to use the money to continue building out its technology, developing apps, expanding into new markets like China, and working on the next generation of its headset, the Meta 3 — according to a short statement announcing the round. The news comes just ahead of the E3 gaming conference kicking off this week, where we may see yet more AR and VR news emerge.
This latest round includes investments from Horizons Ventures Limited (which led its $23 million Series A round), as well as a list that includes several strategic backers with several specifically out of China: Lenovo, Tencent, Banyan Capital, Comcast Ventures, and GQY.
Meta is not disclosing its valuation, but filing documents provided to us by VC Expertspoints to a valuation of up to $307 million post-money for this latest round (the actual valuation depends on how many of the authorized preferred as well as common shares were issued). The Series B originally started as a $40 million round and then expanded before it closed.
Meta was founded in 2012 and is based out of Redwood City, but also has an R&D operation in Israel, where its founders hail from originally.
Many VR and AR companies tend to focus on the software end of the spectrum, developing content and technology to produce more engaging and realistic (and potentially less nauseating) experiences not just for smartphones and other screens but newer products like the Oculus Rift, Samsung VR and HTC Vive — devices that appear to be taking a lead in this still-nascent market to tap into more immersive games and other consumer media, as well as more practical enterprise applications.
Some of the most interesting of that group of software startups are getting snapped up by companies that want to make a mark in this area.
Meta is taking a different route: a vertically integrated approach in which it is using its own software development (which is heavy on computer vision, machine learning, and AI based on neuroscience) that works on hardware of its own design, which lets you immerse yourself in virtual situations that are embedded in real environments, giving you the ability to manipulate the virtual elements with gestures and other hand movements.
Taking the vertical route a road less travelled, but not entirely unpopulated. In addition to the likes of Facebook-owned Oculus, apparently Magic Leap — which is still in stealth but nonetheless valued at $4.5 billion after its last round — is also building its AR approach end-to-end, and from the ground up.
Interestingly, the investors think that Meta, despite its far more modest fundraising, could give Magic Leap a run for its money.
“In our view, Meta has built a world-class team,” said Bin Yue, Founding Partner of Banyan Capital, in a statement. “Meta is probably the only startup which has the capabilities to compete with giant companies’ projects like Microsoft Hololens and Magic Leap.”
Back when Meta was more of an idea than a publicly available product, I met Meron Gribetz, Meta’s CEO, for a demo of its prototypes and saw that he had an incredibly focused and singular vision of how he wanted to develop the company. The headset they were working on, he said at the time, was something they wanted to be easy enough to use that it could be attainable by the mass market. That was years ago, and so it’s great to see them coming along so far.
“It is incredibly gratifying to have the support of big thinkers and investors who understand the importance of creating a new human-computer interface, anchored in science. Our… investors really get what we’re doing and why Meta is different from the other players in AR,” he said in a statement today. “They understand that the combination of our advanced optical engines along with our neuroscience-based interface design approach are what will create a computing experience that is 100 times easier to use and more powerful than traditional form factors.”
Meta’s funding is a sign of how investors are keen to get in early in what is still far from a mainstream industry, but also a mark of how no one is quite sure which way it will develop.
“Augmented reality represents a transformational platform for communication, collaboration and how individuals will work in the future,” said Michael Yang, Managing Director at Comcast Ventures, in a statement. “Meta’s platform enables a host of new ways to conduct business across a wide array of industries. We look forward to supporting Meta as our first investment in the AR market.”
While several of the investors in this round are based out of China, the GQY involvement in particular will see Meta making some significant inroads to China.
“Through the investment in Meta, GQY is looking to bring the best-in-class Augmented Reality applications to China,” said Jier Yuan, VP, North America, GQY, in a statement. “This goal will be achieved by leveraging Meta’s leading-edge AR hardware, software and GQY’s in-depth knowledge and relationships in industrial training, public transportation and education sectors in China.”
Who needs the sun? Many Silicon Valley startups pride themselves on long, grueling work hours, toiling from the crack of dawn until late into the night. But that work pride has a toll — they likely suffer from a serious lack of vitamin D.
Most U.S. office workers do these days and Nootrobox co-founder Geoff Woo says that’s affecting our brains.
Vitamin D plays an important role in calcium absorption, boosts your immune system and may protect against certain forms of cancer, type 1 diabetes, and multiple sclerosis. Without enough of it, you may experience a sort of brain fog and lowered cognitive function.
Startups like Nootrobox, Nootroo and other “smart drug” subscription services started popping up in the last year to help Silicon Valley workers boost brain power. They join a growing cottage industry of products in the biohacking movement, like Bulletproof coffee, smart cocoa and even small doses of psilocybin (hallucinogenic mushrooms) aiming to give those in startup land a performance-enhancing leg up.
Woo and his co-founder Michael Brandt initially released a “stack” of chemically enhanced pills promising to help people wake up, focus and sleep better. The startup pulled in Andreessen Horowitz, Yahoo’s Marissa Mayer and Zynga founder Mark Pincus as investors to help launch the subscription service in 2015. Nootrobox later released “Go Cubes,” chewable coffee cubes, which offer an “evened-out” caffeine kick.
Now Woo wants to add vitamin D (which is actually a hormone, not a vitamin) to boost brain power, too.
Nootrobox is launching KADO-3, a fish oil and krill oil blend with a specifically high ratio of DHA (docosahexaenoic acid) to EPA (eicosapentaenoic acid) to provide a premium vitamin D designed for cognitive function.
Though recommendations differ on the amount we need in our daily diet, the interest in the vitamin goes all the way to the White House. First Lady Michelle Obama recently convinced the FDA to include vitamin D levels on the new food labels.
But pop in at any local nutrition store and there’s plenty of vitamin D sitting on the shelves. Woo says what’s in those bottles is different from what he’s offering.
“Typical Omega-3 blends have a high ratio of EPA to DHA. EPA is associated with cardiovascular benefits while DHA is associated with brain benefits. Whereas Nootrobox ramps up the ratio of DHA to EPA,” says Woo.
The blend also adds Astaxanthin, a neuroprotective that could help manage the risks of geriatric conditions, and vitamin K2, which also has been shown to benefit both the cardiovascular system and the brain.
Woo also revealed the company plans to eventually create a personalized blend based on someone’s DNA to enhance their overall function and Nootrobox will likely partner with one of the larger genetic testing companies to provide such a product, but he said he is not in talks with anyone and that offering won’t be available anytime soon.
The world is on a quest to create cleaner, quieter airplanes that could replace the fuel-guzzling, roaring commercial aircraft in use today.
NASA is leading much of the research and development effort in this area and today they’ve announced an official name for their next X-plane concept: the X-57 “Maxwell.”
Maxwell is a hybrid electric research plane equipped with 14 electric propeller-turning motors located along the wings. The experimental plane will be put through a number of tests over the next four years in an effort to demonstrate that electrical propulsion can make planes quieter, more efficient, and environmentally friendly.
During take-off and landing, Maxwell will make use of all 14 motors to create sufficient thrust, but once it’s up in the air it will only use the two larger motors located on the tips of the wings.
NASA engineers believe that this unique design will result in a five-time reduction of the energy required for a small, private planes to cruise at 175 mph.
Maxwell is a result of NASA’s “New Aviation Horizons” initiative: a 10-year program to create a new generation of X-planes that will make use of greener energy, use half as much fuel, and be half as loud as commercial aircraft in use today. In the President’s FY 2017 budget, NASA received $790 million to fund New Aviation Horizons among other similar green-aviation initiatives.
An Electrically Powered Future
Electrical airplanes are sure to change the look of aviation, but if humans plan to continue to fly in the future we’ll have to embrace this new era of flight.
According to some reports, the world only contains enough petroleum resources to last us through the year 2100. And as we get closer to that date, fuel prices are likely to rise higher and higher.
Eventually, we’ll need to wean ourselves off of internal combustion engines and the aircraft that use them. To do that, we’ll need to see innovation in aircraft design, battery technology, solar cells and electrically powered engines themselves.
In addition to NASA, companies like Boeing and Airbus, and even other countries have started investing in this future. Planes like the GL-10, Helios, E-Fan, and Solar Impulse have already demonstrated the feasibility of electric planes.
Even Elon Musk has suggested that he has plans to come up with an electrically powered jet. When asked to talk about his “next great idea” Musk responded, “Well I have been thinking about the vertical takeoff and landing electric jet a bit more. I mean, I think I have something that might close. I’m quite tempted to do something about it.”
NASA’s Maxwell is the first X-plane in a decade, but the agency has plans for as many as five more through New Aviation Horizons. The technologies and knowledge gained through this initiative will be passed on to the private sector with the hope that, eventually, electric airplanes will become commonplace and transform flying as we know it.
Even the most advanced security teams have a hard time staying ahead of cyberattacks. That’s why Cylance has developed artificial intelligence algorithms to seek out vulnerabilities in computer networks and address them. The Irvine, Calif., company is growing fast and has raised a nine-figure Series D to grow even faster.
CylanceProtect identifies and prevents zero days — that is, holes in software not yet known to the vendor — and other malware and advanced threats, thus protecting customers from downtime, distraction and brand tarnishing. The company says it is serving more than 1,000 customers, growing 785 percent in users and 1,089 percent in product billings since its introduction, “propelling the company to achieve its mission many years earlier than anticipated.”
Cylance has closed $100 million in financing to expand its sales, marketing and engineering programs for its endpoint protection go-to-market strategies. Funds managed by Blackstone Tactical Opportunities and Insight Venture Partners led the round, with follow-on investments by the company’s existing investors, which include DFJ Growth, Fairhaven Capital Partners, and Khosla Ventures, as well as the Blackstone Group, Capital One Growth Ventures, Dell Ventures, Draper Nexus Ventures, KKR & Co. and Ten Eleven Ventures.
Including this latest round, Cylance has raised $177 million total.
“We founded Cylance almost four years ago with a singular mission: protect those who cannot protect themselves, and empower those who can,” CEO Stuart McClure said in a statement. “Our goal of reinventing endpoint security by using machine learning to think like a cyber hacker has been achieved, and we now must ensure that it is put in the hands of security leaders inside enterprises, organizations, governments and small businesses as quickly as possible.”
McClure previously sold his cybersecurity firm Foundstone in 2004 to McAfee, where he spent several years as an executive, including as CTO, before founding Cylance.
“Cylance’s strong track record, including at Blackstone portfolio companies, deepens our conviction in the value that this platform can offer across sectors,” said Viral Patel, a managing director in Blackstone’s Tactical Opportunities.
“Cylance’s team and technology have delivered on exactly what they’ve promised: elegant and effective prevention at the endpoint, in addition to impressive customers and growth,” added Insight managing director Mike Triplett.
If there’s one technology that promises to change the world more than any other over the next several decades, it’s (arguably) machine learning.
By enabling computers to learn certain things more efficiently than humans, and discover certain things that humans cannot, machine learning promises to bring increasing intelligence to software everywhere and enable computers to develop new capabilities –- from driving cars to diagnosing disease –- that were previously thought to be impossible.
While most of the core algorithms that drive machine learning have been around for decades, what has magnified its promise so dramatically in recent years is the extraordinary growth of the two fuels that power these algorithms – data and computing power.
Both continue to grow at exponential rates, suggesting that machine learning is at the beginning of a very long and productive run.
As revolutionary as machine learning will be, its impact will be highly asymmetric. While most machine learning algorithms, libraries and tools are in the public domain and computing power is a widely available commodity, data ownership is highly concentrated.
This means that machine learning will likely have a barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion’s share of their industry’s data.
For startups seeking to take advantage of the machine learning revolution, this barbell effect is a helpful lens to look for the biggest business opportunities. While there will be many new kinds of startups that machine learning will enable, the most promising will likely cluster around the incumbent end of the barbell.
Democratization of Basic Intelligence:
One of machine learning’s most lasting areas of impact will be to democratize basic intelligence through the commoditization of an increasingly sophisticated set of semantic and analytic services, most of which will be offered for free, enabling step-function changes in software capabilities. These services today include image recognition, translation and natural language processing and will ultimately include more advanced forms of interpretation and reasoning.
Software will become smarter, more anticipatory and more personalized, and we will increasingly be able to access it through whatever interface we prefer – chat, voice, mobile application, web, or others yet to be developed. Beneficiaries will include technology developers and users of all kinds.
This burst of new intelligent services will give rise to a boom in new startups that use them to create new products and services that weren’t previously cost effective or possible. Image recognition, for example, will enable new kinds of visual shopping applications. Facial recognition will enable new kinds of authentication and security applications. Analytic applications will grow ever more sophisticated in their ability to identify meaningful patterns and predict outcomes.
Startups that end up competing directly with this new set of intelligent services will be in a difficult spot. Competition in machine learning can be close to perfect, wiping out any potential margin, and it is unlikely many startups will be able to acquire data sets to match Google or other consumer platforms for the services they offer. Some of these startups may be bought for the asset values of their teams and technologies (which at the moment are quite high), but most will have to change tack in order to survive.
This end of the barbell effect is being accelerated by open source efforts such as OpenAI as well as by the decision of large consumer platforms, led by Google with TensorFlow, to open source their artificial intelligence software and offer machine learning-driven services for free, as a means of both selling additional products and acquiring additional data.
Concentration of Higher-Order Intelligence:
At the other end of the barbell, machine learning will have a deeply monopoly-inducing or monopoly-enhancing effect, enabling companies that have or have access to highly differentiated data sets to develop capabilities that are difficult or impossible for others to develop.
The primary beneficiaries at this end of the spectrum will be the same large consumer platforms offering free services such as Google, as well as other enterprises in concentrated industries that have highly differentiated data sets.
Large consumer platforms already use machine learning to take advantage of their immense proprietary data to power core competencies in ways that others cannot replicate – Google with search, Facebook with its newsfeed, Netflix with recommendations and Amazon with pricing.
Incumbents with large proprietary data sets in more traditional industries are beginning to follow suit. Financial services firms, for example, are beginning to use machine learning to take advantage of their data to deepen core competencies in areas such as fraud detection, and ultimately they will seek to do so in underwriting as well. Retail companies will seek to use machine learning in areas such as segmentation, pricing and recommendations and healthcare providers in diagnosis.
Most large enterprises, however, will not be able to develop these machine learning-driven competencies on their own. This opens an interesting third set of beneficiaries at the incumbent end of the barbell: startups that develop machine learning-driven services in partnership with large incumbents based on these incumbents’ data.
Where the Biggest Startup Opportunities Are:
The most successful machine learning startups will likely result from creative partnerships and customer relationships at this end of the barbell.
The magic ingredient for creating revolutionary new machine learning services is extraordinarily large and rich data sets. Proprietary algorithms can help, but they are secondary in importance to the data sets themselves.
What’s critical to making these services highly defensible is privileged access to these data sets. If possession is nine tenths of the law, privileged access to dominant industry data sets is at least half the ballgame in developing the most valuable machine learning services.
The dramatic rise of Google provides a glimpse into what this kind of privileged access can enable.
What allowed Google to rapidly take over the search market was not primarily its PageRank algorithm or clean interface, but these factors in combination with its early access to the data sets of AOL and Yahoo, which enabled it to train PageRank on the best available data on the planet and become substantially better at determining search relevance than any other product.
Google ultimately chose to use this capability to compete directly with its partners, a playbook that is unlikely to be possible today since most consumer platforms have learned from this example and put legal barriers in place to prevent it from happening to them.
There are, however, a number of successful playbooks to create more durable data partnerships with incumbents.
In consumer industries dominated by large platform players, the winning playbook in recent years has been to partner with one or ideally multiple platforms to provide solutions for enterprise customers that the platforms were not planning (or, due to the cross-platform nature of the solutions, were not able) to provide on their own, as companies such as Sprinklr, Hootsuite and Dataminr have done.
The benefits to platforms in these partnerships include new revenue streams, new learning about their data capabilities and broader enterprise dependency on their data sets.
In concentrated industries dominated not by platforms but by a cluster of more traditional enterprises, the most successful playbook has been to offer data-intensive software or advertising solutions that provide access to incumbents’ customer data, as Palantir, IBM Watson, Fair Isaac, AppNexus and Intent Media have done. If a company gets access to the data of a significant share of incumbents, it will be able to create products and services that will be difficult for others to replicate.
New playbooks are continuing to emerge, including creating strategic products for incumbents or using exclusive data leases in exchange for the right to use incumbents’ data to develop non-competitive offerings.
Of course the best playbook of all — where possible — is for startups to grow fast enough and generate sufficiently large data sets in new markets to become incumbents themselves and forego dependencies on others (as, for example, Tesla has done for the emerging field of autonomous driving).
This tends to be the exception rather than the rule, however, which means most machine learning startups need to look to partnerships or large customers to achieve defensibility and scale.
Machine learning startups should be particularly creative when it comes to exploring partnership structures as well as financial arrangements to govern them – including discounts, revenue shares, performance-based warrants and strategic investments. In a world where large data sets are becoming increasingly valuable to outside parties, it is likely that such structures and arrangements will continue to evolve rapidly.
Perhaps most importantly, startups seeking to take advantage of the machine learning revolution should move quickly, because many top technology entrepreneurs have woken up to the scale of the business opportunities this revolution creates, and there is a significant first-mover advantage to get access to the most attractive data sets.
Tech companies have become expert at analyzing consumer shopping patterns on websites. But the next frontier is observing how people shop in old-fashioned brick-and-mortar retail stores, and a growing number of companies, from startups to giants like Facebook, are tackling the problem.
On Wednesday, xAd unveiled a new service that tracks foot traffic to real world stores and serves up the information to businesses through an online dashboard.
The company can tell when consumers walk into individual stores thanks to partnerships it has struck with more than 100,000 smartphone apps. The apps relay GPS location information, which xAd aggregates and makes anonymous to measure and analyze who is shopping at different stores. (xAd says it works with its app partners to ensure that all the location data it collects is done with the necessary permissions from users.)
The new service, called MarketPlace Discover, has been tested by Taco Bell and several other major brands, according to the company.
Facebook is also looking to bridge the gap between offline shopping and its trove of ad-targeting data. On Tuesday the company announced new features to let retailers provide maps to their stores within the ads that appear on the social network. Facebook will also be able to measure the number of people who actually visit the stores using its location features.
Slowly but surely, cyber security is evolving from the days of castles and moats into the modern era of software driven business. In the 1990s, after several failed attempts to build secure operating systems, the predominant security model became the network-perimeter security model enforced by firewalls. The way it works is clear: Machines on the inside of the firewall were trusted, and anything on the outside was untrusted. This castle-and-moat approach failed almost as quickly as it began, because holes in the wall had to be created to allow emerging internet services like mNews, email and web traffic through.
Artificial intelligence will replace large teams of tier-1 SOC analysts who today stare at endless streams of threat alerts.
With a security wall that quickly became like Swiss cheese, machines on both sides were still vulnerable to infection and the antivirus industry emerged to protect them. The model for antivirus then and now is to capture an infection, create a signature, and then distribute it widely to “immunize” other machines from getting infected by the same malware. This worked for vaccines, so why not try for cyber security?
Fast-forward to 2016, and the security industry hasn’t changed much. The large security companies still pitch the castle-and-moat model of security — firewalls and signature-based detection — even though employees work outside the perimeter as much as inside. And in spite of the fact that most attacks today use one-and-done exploit kits, never reusing the same malware again. In other words, the modern work force coupled with modern threats has rendered traditional security techniques obsolete.
Software is eating security
While most enterprises today still employ these dated security techniques, a new model of security based on artificial intelligence (AI) is beginning to take root in organizations with advanced security programs. Necessity is the mother of invention, and the necessity for AI in security became obvious when three phenomena emerged: (1) The failure of signature-based techniques to stop current threats; (2) the voluminous amounts of security threat data; and (3) the scalability challenges in addressing security threat data with people.
“Software is eating the world,” the noted venture capitalist Marc Andreessen famously said in 2011 about such obvious examples as Amazon, Uber and Airbnb disrupting traditional retail and consumer businesses. The security industry is ripe for the same kind of disruption in the enterprise space, and ultimately in the consumer product space. Artificial intelligence will replace large teams of tier-1 SOC analysts who today stare at endless streams of threat alerts. Machines are far better than humans at processing vast amounts of data and finding the proverbial needle in the haystack.
Artificial Intelligence is experiencing a resurgence in commercial interest because of breakthroughs with deep learning neural networks solving practical problems. We’ve all heard about IBM’s Watson winning at “Jeopardy,” or making difficult medical diagnoses by leveraging artificial intelligence. What is less well known is that Watson has recently undergone a major deep learning upgrade, as well, allowing it to translate to and from many languages, as well as perform text to speech and speech to text operations flawlessly.
Many of us interact with deep learning algorithms unwittingly when we see TV show and movie recommendations on Netflix based on what we’ve viewed previously or when your Mac properly identifies everyone in a picture uploaded from your phone. Or when we ask Alexa a question and Amazon Echo gives an intelligent response — likewise for Cortana and Siri. And one of the most hotly debated topics in machine learning these days is self-driving cars, like Tesla’s amazing Model S.
Deep learning allows a machine to think more like a human. For instance, a child can easily distinguish a dog from a cat. But to a machine, a dog is just a set of pixels and so is a cat, which makes the process of distinguishing them very hard for a machine. Deep learning algorithms can train on millions of pictures of cats and dogs so that when your in-house security camera sees the dog in your house, it will know that it was Rover, not Garfield, who knocked over the vase.
With deep learning, today’s next-generation security products can identify and kill malware as fast as the bad guys can create it.
The power of deep learning becomes clear when you consider the vast speed and processing power of modern computers. For instance, it takes a child a few years to learn the difference between a house cat and a dog. And if that child grew up to be a cat “expert,” it would take Gladwell’s 10,000 hours to become a feline whisperer. The amount of time it takes to expose a human to all of the training data necessary to classify animals with near perfection is long. In contrast, a deep learning algorithm paired with elastic cloud computing resources can consume hundreds of millions of samples of training data in hours, to create a neural network classifier so accurate and so fast that it would outperform even the most highly trained human experts.
What’s more fascinating than this new technology allowing machines to think like a human, is allowing machines to act like a human. Since the 1950s, we’ve been fascinated with the notion that robots might one day be able to think, act and interact with us as our equals. With advances in deep learning, we’re one giant step closer to that reality. Take the Google Brain Team’s DeepDream research, for instance, which shows that machines trained in deep learning can create beautiful pieces of art, in a bizarre form of psychedelic machine “dreaming.” For the first time, we see incredible creativity from machines because of deep learning, as well as the ability to make decisions with incredible accuracy.
Because of this ability to make classification decisions with incredible accuracy, deep learning is leading a renaissance in security technologies by using the technology to identify unknown malware from benign programs. Like the examples above, this is being done by training the deep learning neural networks on tens of millions of variants of malware, as well as on a representative sample of known benign programs.
The results are industry-changing, because unlike legacy security products that provided protection either through prior knowledge of a threat (signature-based) or via segmentation and separation, today’s next-generation security products can identify and kill malware as fast as the bad guys can create it. Imagine a world where security technologies actually enable more sharing rather than less, and allow a more open approach to data access rather than restrictive. This is the direction deep learning is allowing us to go.
Are you ready?
Disruption is clearly coming to the security space. The market has been waiting for better technology that can keep pace with the fast-evolving adversarial threat. Breakthroughs in deep learning artificial neural networks are now stopping attacks previously unseen in real time before they even have a chance to run. It’s time to get on-board with a new generation of technology that is disrupting traditional castle-and-moat security models.
Tata Communications launched the 2016 F1 Connectivity Innovation Prize, focusing on how virtual reality (VR) or augmented reality (AR) technologies could be used to make the sport more immersive for fans, and help the teams work more effectively together in the run-up to and during each Grand Prix.
The aim of the $50,000 prize is to inspire fans worldwide to harness their technical know-how and passion for F1 racing to drive innovation in the sport through two technology challenges.
Tata Communications is the Official Connectivity Provider of Formula 1, enabling the sport to seamlessly reach its tens of millions of fans across the globe.
The first challenge, set by Formula One Management, calls on technology enthusiasts to develop a solution that uses VR and AR to enable fans at home to experience a Grand Prix virtually.
The solution should allow fans who are not at the live event to immerse themselves into the exhilarating world of F1 racing – from the pit lane and the Formula One Paddock Club, to the drivers’ parade and the starting grid formation.
“We want to give as many fans as possible the opportunity to experience first-hand the thrill of a Grand Prix – and VR or AR could enable us to do just that,” said John Morrison, CTO of Formula One Management and one of the judges.
“These technologies represent the next big innovation opportunity for the sport. In the not-too-distant future, they could enable fans to get virtually transported to a Grand Prix, complementing and enriching the race experience,” said Morrison.
Julie Woods-Moss, Tata Communications’ CMO and CEO of its NextGen Business, said that in the last two years, the F1 Connectivity Innovation Prize has grown into a major platform for showcasing the huge potential of data and superfast connectivity in boosting F1 teams’ competitiveness, and in bringing fans closer to the sport
“We now invite fans from all over the world to share their ideas for how VR and AR could take fan engagement to the next level,” she said.