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Malta AI & Blockchain Summit invites start-up trailblazers

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Malta AI & Blockchain Summit invites start-up trailblazers
Best start-ups across AI sector to battle it out for prestigious rewards

Leading start-ups from the artificial intelligence industry will compete for a business boost at the winter edition of Malta AI & Blockchain Summit this November. The now world-renowned Malta AIBC Summit will take place 7th to 8th November 2019, at the InterContinental Arena Conference Centre, St Julian’s,  Malta, marking the second event in 2019 for the successful expo.

AI start-ups will be vying for a place amongst the top ten successful start-ups to be selected to participate in the AI Start-up Pitch, where they’ll take part in a live showdown in front of a panel of the most prominent experts from the AI industry and investors.  Taking place on the main stage at peak time during day one of the event, the AI Pitch is always a favourite with the AIBC audience.

Malta AI & Blockchain Summit invites start-up trailblazers

With a prize package including a year’s office space, media relations support, digital marketing,  and more, one lucky start-up will get a massive head start with their business plans – the stakes could not be higher as the Malta AIBC Summit continues to inspire and nurture the incredible talent appearing on this emerging tech scene.

Of course, all participants are set to be winners, as with such luminary figures from the sector judging the contest, taking part all but guarantees invaluable insights from critical feedback and praise of unique applications for artificial intelligence. And with investors aplenty in attendance, it should be no surprise that many start-ups will come away from the summit with a bright future looming!

100 deep tech start-ups from the sectors of AI, Blockchain, IoT, and Big Data will also showcase their projects and services with a 1×1 stand in the start-up village, benefiting both from 1000s of attendees at the show, as well as online visibility at AIBCsummit.com.

Eman Pulis, founder and CEO of the Malta AI & Blockchain Summit, commented, “In this, our third and biggest show yet, we can’t wait to see the range and quality of entrants for the AI Start-Up Pitch.  If our previous battles are anything to go by, we will be facilitating the seeds of growth for some of the future global brands in the AI space. I couldn’t be prouder that our show is instrumental in bringing together such amazing talent in one venue, and I look forward to meeting all those taking part.”

To book tickets for Malta A.I. & Blockchain Summit, click here.

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For further information, please contact: sofya@maltablockchainsummit.com

 

About AIBC:

Malta AI & Blockchain Summit is a bi-annual expo covering topics relating to the global sectors for blockchain, AI, Big Data, IoT, and Quantum technologies.  The event includes conferences hosted by globally renowned speakers, workshops for industry learning and discussion, an exhibit space accommodating more than 400 brands and much more.

The first Malta Blockchain Summit took place in November 2018 at the Intercontinental Hotel in St Julians, Malta, attracting 8,500 attendees from over 80 countries worldwide, with 300 sponsors and exhibitors, 200 speakers, and 1 AI VIP (Sophia the world’s first robot citizen). With strong support from the Maltese government, the event has quickly established itself as one of the world’s leading destinations for the growing sectors of AI, Blockchain and DLT, IoT, and other vertical industries. At the 2018 event the Maltese government introduced 3 new bills to support the growth of the sector and promoting Malta as the “Blockchain Island”.


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Antoine Tardif is the CEO of BlockVentures.com, and has invested in over 50 blockchain projects. He is also the founder of bitcoinlightning.com a news website focusing on the lightning network, and a founding partner of unite.ai

Healthcare

Paper Examines How To Reduce Risk Of Using AI in Medicine

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Paper Examines How To Reduce Risk Of Using AI in Medicine

Artificial intelligence programs are capable of improving healthcare in a variety of different ways. For instance, AI applications can use computer vision to help doctors diagnose conditions from X-rays and FMRIs. Machine learning algorithms can also be used to help reduce false-positive rates by extracting subtle patterns from data that humans may not be able to find in medical data. However, with the possibilities comes new challenges, and recently a new article was published in Science that examined possible risks and regulatory strategies for medical machine learning techniques in an effort to minimize any possible negative side effects of employing AI in a medical context.

Expanding Applications For AI In Healthcare

AI is seeing its applications in the medical field expand rapidly.  Recent developments in the field of healthcare, driven by AI, include the creation of a new pharmaceutical company that aims to use AI to create new drugs, the creation of AI-drive remote health sensors, and computer vision apps that analyze CT scans and X-rays.

To be more precise, Genesis Therapeutics is a startup that is aiming to use AI to speed up the process of drug-discovery, hoping to create drugs that can reduce the severity of debilitating diseases. Genesis Therapeutics is just one of almost 170 different firms using AI to research new drug formulations. Meanwhile, in terms of health monitoring devices, iRhythm and French AI startup Cardiologs are making use of AI algorithms to analyze EEG data and monitor the health of those who have heart conditions are at risk of complications. The software designed by the companies can detect heart murmurs, a condition caused by turbulent blood flow.

Finally, a recent study investigating how computer vision can be applied to medical images found that computer vision systems perform at least as well or better than expert radiologists when examining CT scans to find small hemorrhages. The algorithms used in the study were able to render predictions after examining CT scans for just one second. The computer vision systems were also able to localize the hemorrhage within the brain.

So while the potential benefits of using AI in healthcare are clear, what is less clear is what new challenges and risks will arise as a side-effect of employing AI within the healthcare field.

Regulating An Expanding Field

As TechXplore reported, in order to assess potential drawbacks of using AI in healthcare,  a group of researches recently published a paper in Science, aiming to derive answers to anticipate potential problems with AI and explore potential solutions to these problems. Problems that may arise from using AI in the healthcare field include the inappropriate recommendation of treatments resulting in injury, privacy concerns, and algorithmic bias/inequality.

The FDA has only approved medical AI that uses “locked algorithms”, algorithms that reliably produce the same result every time they are run. However, much of AI’s potential lies in its ability to learn and respond to new types of inputs. In order to enable “adaptive algorithms” to see more use and get approval from the FDA, the authors of the paper took an in-depth look at how the risks related to updating algorithms can be mitigated.

The authors advocate that machine learning engineers and researchers should focus on continuous monitoring of models over the lifetime of their deployment. Among the suggested tools to monitor AI systems was AI itself, which could help give automated reports on how an AI is behaving. It’s also possible that multiple AI devices could monitor each other.

“To manage the risks, regulators should focus particularly on continuous monitoring and risk assessment, and less on planning for future algorithm changes,” said the authors of the paper.

The authors of the paper also recommend that regulators focus on developing new methods of identifying, monitoring, assessing, and managing risks. The paper applies many of the techniques that the FDA has used to regulate other forms of medical tech.

As the paper’s authors explained:

“Our goal is to emphasise the risks that can arise from unanticipated changes in how medical AI/ML systems react or adapt to their environments. Subtle, often unrecognised parametric updates or new types of data can cause large and costly mistakes.”

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Artificial General Intelligence

Facebook’s AI Takes on Hanabi Game

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Facebook’s AI Takes on Hanabi Game

Facebook AI Research (FAIR) has developed a new AI that produced extremely impressive results when put up against Hanabi. The new development is a major step forward for Facebook’s AI. 

Hanabi is a card game similar to Solitaire. While most games that are used for this technology place AI against humans directly, specifically chess or Go, Hanabi requires players to work with each other towards a common goal. 

Facebook employed bots to work together in the game until they outperformed previously used AI systems. The most recent best AI system achieved a score of 23.92 out of 25 in the game, while the new one reached 24.61 out of 25. 

Back in February, A Hanabi benchmark was proposed by researchers from Google, DeepMind, Carnegie Mellon University, and Oxford. They also included the creation of additional AI capable of playing the game, and they called it “a new frontier for AI research.” 

Researchers are excited about the new development since the same AI used to help the bots could possibly be used in other areas. One possible use is to improve the way that virtual assistants interact with people. 

Noam Brown, a Facebook AI researcher, spoke about the new AI system. 

“One of the really exciting things about this is that the improvement we’re observing is really orthogonal to the improvements that are being observed with deep reinforcement learning: You can add this on top of any strategy, and it will make it much stronger,” Bown said in an interview he gave to VentureBeat. “We’re seeing that the results are far beyond what we or other researchers expected. In fact, the benefits that we get from search are stronger than the benefits that have been gained through all of the deep reinforcement learning algorithms that have been used in the past.”

The new development with Facebook’s AI comes at a time when researchers are continuing to create software capable of going up against some of the most complex games. In 2016, Google’s DeepMind’s AI system beat the best human players in the Chinese board game Go. 

Hanabi is now considered the best game for testing AI since it is built around teamwork and strategy, a major milestone for AI to reach. When used in this environment, AI can improve and become more sophisticated.

Adam Lerer is a Facebook researcher and contributor to the paper. 

“One of the reasons we’re moving to these cooperative games is that I think we’re kind of at the point where there’s no games left at least in terms of competitive games,” he said. 

Hanabi has teams of two to five players who are given random cards. The cards are different colors and contain different numbers, and the teams place them on a table, by color and in the correct numerical order. 

Players are not able to see their own cards, but their teammates can. Players are permitted to give hints to others. For example, a teammate can give a hint about colors, leading to the other to play or discard the card. 

One of the more complex aspects of the game is that a player has to figure out the clues and what they mean. This part of the game is difficult for a bot to figure out with the information that they have. 

The bots were able to build a strategy due to the techniques and reinforcement learning that Facebook used. Facebook believes that this technology could be used in other applications like robotics, self-driving vehicles, and other systems. 

“This is something that comes very naturally to humans, this idea of being able to put yourself in the shoes of another person and understand why they’re taking the actions they’re taking, what they’re thinking, and even if they don’t know certain things. But it’s something that AI has historically really struggled with,” he said. “There’s been this long debate about whether primates have theory of mind and at what age do humans babies develop theory of mind, and I think it’s really fascinating to finally be seeing this sort of behavior in AI. And I think that that’s going to be really important if we want to deploy AI in the real world to interact with humans because humans expect this behavior.”

 

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Startups

AI Can Help Make Wildfires Quicker To Spot And Easier To Fight

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AI Can Help Make Wildfires Quicker To Spot And Easier To Fight

In states like California, the wildfire season has become longer and more intense, driven largely by climate change. In response to the growing threat from wildfires, according to CNN, various startups have created AI tools intended to assist in the detection of wildfires.

It may seem obvious, but early detection is important for wildfires. The earlier the blaze is detected the faster it can be contained and the less damage it will do. Thankfully, the AI tools designed by companies like Descartes Labs, based in Sante Fe, seem to be more effective at detecting wildfires than either firefighters or civilians.

The Fire-detecting tool from Descartes Labs samples images from government weather satellites every two minutes, comparing the images for differences. If there is any difference in the thermal signals in a region, it could potentially indicate the presence of a wildfire.

Current methods of detecting wildfires rely primarily on spotting fire with either planes or lookout towers, but a system that makes use of AI and satellites can detect wildfires much quicker than these methods. The New Mexico State Forestry Bureaus has stated that the AI tool has definitely helped the state locate wildfires much more quickly than before. The tool also provides first responders with descriptions that can help narrow down where a fire is, which can be difficult when there is a lot of smoke or over a mountain range at night.

Descartes isn’t the only company to try and use AI to detect forest fires. Northrop Grumman recently started a contract with the state of Calfornia to design wildfire analysis tools, and the startup Technosylva has also invested in the creation of wildfire prediction methods.

It isn’t clear yet if the technologies designed by these companies may increase the risk of false alarms as a result of increased sensitivity to possible fires. However, what is clear is that the AI tools designed by Descartes can genuinely detect forest fires much earlier than even some of the best currently exiting fire detection methods. For example, Descartes states that their detection systems were able to alert the Los Angeles Times to the coordinates of the Kincade fire very shortly after the fire started. Descartes states that so far their quickest detection time is nine minutes after the ignition of the fire. As reported by CNN, Ernesto Alvarado, wildfire expert and researcher at the University of Washington, any system that is able to detect a fire in under 30 minutes after the ignition is pretty impressive.

Descartes is beginning to explore other methods of using AI and data to help detect and track fires. For instance, the company is in the process of designing digital elevation models that can describe steep slopes that could hinder firefighting efforts. Descartes is accomplishing this by using a variety of algorithms that each vote on the position of a fire on a map and come to a consensus.

While the tools developed by Descartes and others may prove effective at enabling the quicker detection of fires, getting fire response teams into position is a challenge all its own and unless this problem is solved, fire detection algorithms may not be as effective as theoretically possible. As an example, even after a potential fire is flagged by Descartes’ tools, the fire has to be forwarded on to the correct authorities, such as a field office that can verify the existence of the fire. After this, the notification must go out to fire departments in the area who must assess the best way to respond to the fire. These logistical challenges may impose limits on just how effective fire-detection systems can be, but even so, when it comes to detecting fires, earlier is always better.

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