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Year after year, motorcycling has changed and continues to change, going hand in hand with technological progress, and as expected yet another step forward is just around the corner. Its strange to talk about technology and think of the bikes raced by legends like Angel Nieto or Eddie Lawson, but everything evolves and the world of two wheels is no exception.

Ducati has been talking about the next step in technological terms, in a telematic meeting organized together with its partner Lenovo and open to the press: the fact that manufacturers try to improve the performance of the bike by digging into every scientific-technical area is well-known, but the next move will be none other than artificial intelligence.

The concept was explained by Gabriele Conti, head of Ducatis electronic systems department. "It is no longer just about collecting data, but also about analysing it in depth, and the next step will involve the use of Artificial Intelligence. I think it's the future, because we need something that thinks faster than humans, which artificial intelligence can do. Among other things, we are already using a machine to learn. "

Conti continued in his analysis, accompanying us on a little walk into the future

We already use some parameters calculated with a machine, which is able to manage an incredible amount of data, something that we just cannot do. It performs correct calculations and manages to obtain parameters in real time. The next step, and we are already working on it, will help track and factory engineers to develop the new bike ".

But what does a rider think of all this? The answer was provided by Danilo Petrucci, who confirmed that the riders are now focussing on the study of data.

Unfortunately, we have to spend more time looking at the data than riding a motorbike: it is a way to understand how to ride better, 80% of the bike's configuration is based on data. In this sense, for every Grand Prix I receive from my chief engineer a seven-page email with the data: this has been the case since 2015, I 've still saved them all. " Ladies and gentlemen, welcome to the future.

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MotoGP, Ducati in the future: artificial intelligence to go faster - GPone English

The promise of clean, green nuclear fusion has been touted for decades, but the rise of AI means the challenges could finally be overcome

By Abigail Beall

THE big joke about sustainable nuclear fusion is that it has always been 30 years away. Like any joke, it contains a kernel of truth. The dream of harnessing the reaction that powers the sun was big news in the 1950s, just around the corner in the 1980s, and the hottest bet of the past decade.

But time is running out. Our demand for energy is burning up the planet, depleting its resources and risking damaging Earth beyond repair. Wind, solar and tidal energy provide some relief, but they are limited and unpredictable. Nuclear fission comes with the dangers of reactor meltdowns and radioactive waste, while hydropower can be ecologically disruptive. Fusion, on the other hand, could provide almost limitless energy without releasing carbon dioxide or producing radioactive waste. It is the dream power source. The perennial question is: can we make it a reality?

Perhaps now, finally, we can. That isnt just because of the myriad fusion start-ups increasingly sensing a lucrative market opportunity just around the corner and challenging the primacy of the traditional big-beast projects. Or just because of innovative approaches, materials and technologies that are fuelling an optimism that we can at last master fusions fiendish complexities. It is also because of the entrance of a new player, one that could change the rules of the game: artificial intelligence. In the right hands, it might make the next 30 years fly by.

Nuclear fusion is the most widespread source of energy in the universe, and one of the most efficient: just a few grams of fuel release the same energy as

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Why cracking nuclear fusion will depend on artificial intelligence - New Scientist

The technology can also report offences such as the failure to observe physical distancing.

Dubai's Roads and Transport Authority announced that Artificial Intelligence (AI) technologies have been employed in taxis to monitor and verify the compliance with the preventive measures undertaken to limit the spread of the coronavirus.

"The technology can also report offences such as the failure to observe physical distancing, and the improper wearing of face masks, thanks to video analysis feature," said Ahmed Mahboub, Executive Director of Smart Services, Corporate Technology Support Services Sector, RTA.

The use of Artificial Intelligence (AI) technologies, such as computer vision and machine learning algorithms, proved very effective in detecting and reporting violations of preventive measures undertaken to fight the Coronavirus (Covid-19). Such monitoring covers physical distancing and wearing of face masks onboard taxis, whether for passengers or drivers.

"The use of AI technologies proved very effective and achieved a success rate of 100%. The introduction of this technology was on a trial base, and according to the deliverables, the technology will be generalised to all fleet vehicles," said Mahboub.

"The experiment highlighted the capability of AI technology in processing video files spanning 200 thousand hours a day. Thus, it reduces the need for human intervention and saves much time and effort that would have otherwise been necessary to analyse these videos," he noted.

Explaining the functionality of onboard devices, Mahboub said, "AI devices were programmed to scan human faces and verify if the mask is worn correctly. The technology has a mathematical feature that calculates the distance between passengers and the driver as well."

"RTA is continuing efforts to play a leading role in implementing the 4th Industrial Revolution technologies in line with the UAE Artificial Intelligence Strategy. The overall objective is to harness technologies to serve the community, and realise RTA's vision of Safe and Smooth Transport for All," concluded Mahboub.

reporters@khaleejtimes.com

Staff Reporter

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Combating coronavirus: RTA employs Artificial Intelligence in taxis to curb spread of Covid-19 - Khaleej Times

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Here's What You Need To Remember:The Army and industry are currently developing algorithms to better enable manned-unmanned teaming among combat vehicles. The idea is to have a robotic wingman, operating in tandem with armored combat vehicles, able to test enemy defenses, find targets, conduct ISR, carry weapons and ammunition or even attack enemies.

The Army is engineering new AI-enabled Hostile Fire Detection sensors for its fleet of armored combat vehicles to identify, track and target incoming enemy small arms fire.

Even if the enemy rounds being fired are from small arms fire and not necessarily an urgent or immediate threat to heavily armored combat vehicles such as an Abrams, Stryker or Bradley, there is naturally great value in quickly finding the location of incoming enemy small arms attacks, Army weapons developers explain.

There are a range of sensors now being explored by Army developers; infrared sensors, for example, are designed to identify the heat signature emerging from enemy fire and, over the years, the Army has also used focal plane array detection technology as well as acoustic sensors.

We are collecting threat signature data and assessing sensors and algorithm performance, Gene Klager, Deputy Director, Ground Combat Systems Division, Night Vision and Electronic Sensors Directorate, told Warrior Maven in an interview last year.

Klagers unit, which works closely with Army acquisition to identify and at times fast-track technology to war, is part of the Armys Communications, Electronics, Research, Development and Engineering Center (CERDEC).

Army senior leaders also told Warrior Maven the service will be further integrating HFD sensors this year, in preparation for more formals testing to follow in 2019.

Enabling counterattack is a fundamental element of this, because being able to ID enemy fire would enable vehicle crews to attack targets from beneath the protection of an armored hatch.

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The Army currently deploys a targeting and attack system called Common Remotely Operated Weapons System, or CROWS; using a display screen, targeting sensors and controls operating externally mounted weapons, CROWS enables soldiers to attack from beneath the protection of armor.

If we get a hostile fire detection, the CROWS could be slued to that location to engage what we call slue to cue, Klager said.

Much of the emerging technology tied to these sensors can be understood in the context of artificial intelligence, or AI. Computer automation, using advanced algorithms and various forms of analytics, can quickly process incoming sensor data to ID a hostile fire signature.

AI also takes other information into account and helps reduce false alarms, Klager explained.

AI developers often explain that computer are able to much more efficiently organize information and perform key procedural functions such as performing checklists or identifying points of relevance; however, many of those same experts also add that human cognition, as something uniquely suited to solving dynamic problems and weighing multiple variables in real time, is nonetheless something still indispensable to most combat operations.

Over the years, there have been a handful of small arms detection technologies tested and incorporated into helicopters; one of them, which first emerged as something the Army was evaluating in 2010 is called Ground Fire Acquisition System, or GFAS.

This system, integrated onto Apache Attack helicopters, uses infrared sensors to ID a muzzle flash or heat signature from an enemy weapon. The location of enemy fire could then be determined by a gateway processor on board the helicopter able to quickly geolocate the attack.

While Klager said there are, without question, similarities between air-combat HFD technologies and those emerging for ground combat vehicles, he did point to some distinct differences.

From ground to ground, you have a lot more moving objects, he said.

Potential integration between HFD and Active Protection Systems is also part of the calculus, Klager explained. APS technology, now being assessed on Army Abrams tanks, Bradleys and Strykers, uses sensors, fire control technology and interceptors to ID and knock out incoming RPGs and ATGMs, among other things. While APS, in concept and application, involves threats larger or more substantial than things like small arms fire, there is great combat utility in synching APS to HFD.

HFD involves the same function that would serve as a cueing sensor as part of an APS system Klager said.

The advantages of this kind of interoperability are multi-faceted. Given that RPGs and ATGMs are often fired from the same location as enemy small arms fire, an ability to track one, the other, or both in real time greatly improves situational awareness and targeting possibilities.

Furthermore, such an initiative is entirely consistent with ongoing Army modernization efforts which increasingly look toward more capable, multi-function sensors. The idea is to have a merged or integrated smaller hardware footprint, coupled with advanced sensing technology, able to perform a wide range of tasks historically performed by multiple separate on-board systems.

Consolidating vehicle technologies and boxes is the primary thrust of an emerging Army combat vehicle C4ISR/EW effort called Victory architecture. Using ethernet networking tech, Victory synthesizes sensors and vehicle systems onto a common, interoperable system. This technology is already showing a massively increased ability to conduct electronic warfare attacks from combat vehicles, among other things.

HFD for ground combat vehicles, when viewed in light of rapidly advancing combat networking technologies, could bring substantial advantages in the realm of unmanned systems. The Army and industry are currently developing algorithms to better enable manned-unmanned teaming among combat vehicles. The idea is to have a robotic wingman, operating in tandem with armored combat vehicles, able to test enemy defenses, find targets, conduct ISR, carry weapons and ammunition or even attack enemies.

All that we are looking at could easily be applicable to an unmanned system, Klager said.

Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army - Acquisition, Logistics& Technology. Osborn has also worked as an anchor and on-air military specialist for National TV networks. He has a Masters degree in Comparative Literature from Columbia University. This article first appeared last year.

This first appeared in Warrior Maven here.

Image: Reuters.

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Originally posted here:
Artificial Intelligence Is Making The Army's Armored Vehicles Deadlier Than Ever - Yahoo News

The way Jordi Cruyff tells the story, it all started with an email. The former Manchester United midfielder was working as the sports director of Maccabi Tel Aviv in 2017 when he was offered a trial with a little-known artificial intelligence company that said it could help the team to prevent injuries. It certainly sounded interesting, and there was nothing to lose, so he decided there was no harm in giving it a go.

The club promptly sent over their squads fitness data and, in return, a warning soon landed in Cruyffs inbox: according to the companys calculations, seven first-team players were at risk of injury. The head coach was not too impressed to be told this, and chose not to act upon it. Five of those players were then injured. It then happened again, and again, and Cruyff soon realised that what was unfolding before him was no coincidence.

Cruyff is now an investor in the company, called Zone7, and more and more clubs across the world are starting to realise that they too might benefit from a technology that could revolutionise the medical side of the sport. With major leagues now preparing for a condensed run of league fixtures, the likes of which they have not seen in the modern era, the need to reduce injuries has become a more pressing problem than ever. And so, as coronavirus put a halt to leagues all over Europe, Zone7 suddenly found they were offering a service that has never been more valuable.

We now have data from over 50 teams across the UK, Italy, France, Germany, Spain and the US, says Tal Brown, Zone7s co-founder and CEO. Brown is based in Silicon Valley and, prior to launching Zone7, founded the first artificial intelligence team at software giant Salesforce. He is not, therefore, what the sport might consider a football person. Neither is the technology what the sport might consider to be football tech.

The instant reaction from many in the game, much like the coach at Maccabi Tel Aviv, is to be sceptical. Silicon Valley technology experts advising football teams how to manage their players? It is not hard to see why there might be some resistance, especially given the stubborn nature of the sport. But the evidence suggests that the Zone7 algorithm works, and that it works spectacularly.

Among the first clubs to sign up were Getafe, who are far from being the most glamorous or wealthy side in La Liga. In two seasons working with Zone7, their injury rate reduced by 70 per cent. Last season, they suffered just eight injuries, by far the lowest in the division, as they punched well above their weight to finish fifth. Atletico Madrid, meanwhile, had 47 injuries. Real Madrid had 32. Javier Vidal, Getafes fitness coach, has described the technology as a tool that will change elite sport.

So how does it work? In its simplest terms, Zone7 uses pattern recognition to rapidly interpret the huge amounts of data that clubs collect on their players. As a sport, we know almost everything that we can know about our athletes, says Brown. We have deployed medical products to measure strength and flexibility, we know how much they are moving around the pitch, we know things about how they play the game. That data is vast.

Digesting it, and converting it into practical information, is therefore a significant challenge. A lot of the interpretation itself is usually left in the hands of the human operator, says Brown. The analysis process relies on these individuals looking at charts, contextualising them and trying to drive decisions.

Using artificial intelligence, Zone7 helps to make those decisions. Rather than a physio having to comb through eight or nine charts per day for 25 first-team players, looking for indicators of fatigue or dips in performance, Zone7s algorithm can do it for them. With all the data stored in its system, it can recognise the patterns that lead to injury.

From there, the clubs are told which players could be at risk and what the next training session should look like for those individuals. An example would be Zone7s algorithm identifying a detrimental pattern in a players sprinting. The club might then be advised that the player should only be doing 400m of high-speed running the next day, rather than 1,000m.

Teams have scientists and physios and strength coaches that are great professionals who know what they are doing, says Eyal Eliakim, the chief technology officer and co-founder. They have to take care of at least 25 players every day in a training programme that is for everyone. It is hard to understand what tweaks are needed and it is really hard to cover all the players. It takes hours and hours every day to manually look at each players data. That is where we come in.

The temptation is to view this as the latest chapter in the data revolution of football, which has led in the past decade to a considerable surge in interest in the statistical analysis of recruitment and tactics. The Moneyball effect, as it is known. On the coaching and medical side of the game, though, things have moved slower until now. The nerds are not taking over, says Brown. But they are making a bigger impact.

The success of Getafe has helped to spread the word about Zone7 and their technology. Leading La Liga clubs are jumping on board and the company is currently in conversation with Premier League teams. Two Championship sides have already signed up, while in the last few weeks alone Zone7 have taken on two new German clients and three teams in Italys Serie A.

Evidently, these sides know the importance of keeping their injury rate low. There is a financial benefit to this, of course, but it is the performance benefit that is so appealing. Getafes success in La Liga tells its own story, although perhaps an even better example would be Leicester Citys title victory in 2016.

For whatever reason (they were not working with Zone7), Claudio Ranieris Leicester enjoyed the most extraordinary season with regard to injuries. Physioroom data shows that in the 2015/16 campaign, Leicesters players lost a total of 275 days to injury, by far the best record in the league. By way of comparison, the league average for the 20 Premier League clubs that year was 1130 days. Being able to consistently deliver high squad availability is a metric that is often overlooked, says Brown.

In their own words, Brown and Eliakim come into this with an outsiders perspective. Their expertise is in artificial intelligence, rather than the sport itself. Indeed, some observers with more of a sporting background remain unconvinced that the platform can have enough information to be truly effective.

I am pretty sceptical of AI and machine learning in injury prediction, says Markus Deutsch, the global CEO of sports technology company Fusion Sport. The AI is not there yet. Its not good enough, and the reason for that is that there is not enough data. You are talking about a very low number of actual injuries over the course of the season. It does not lend itself to good statistical modelling.

Zone7, and their clients, would disagree. And perhaps most excitingly for them, the more teams they sign up, the more advanced the algorithm will be. This becomes inherently better automatically, as it is driven by more data, Brown says. Getafe in season two had better metrics than Getafe in season one, because we had more data. That is also true across the board with more teams.

Inevitably, some of the more traditional clubs will still resist the very concept of distant machine learning as an injury prevention tool. For those who commit as readily as a team like Getafe, though, it could change their approach forever.

Most clubs will just use their data to run their own analytics, which is a great start, says Brown. But if you can have an algorithm looking for injury signals and learning from 10,000 injuries, instead of 100, then that is a different ball-game.

Join our football experts, Jamie Carragher, Jason Burt and Sam Wallace for a discussion and Q&A about the return of the Premier League on Tuesday June 16 at Midday. Sign-up to attend, here.

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The best way to avoid injuries? Top clubs are turning to artificial intelligence - Telegraph.co.uk