The combination of analytics and video is helping coaches to hone player performance and the sport is keen to push innovative use of technology in all areas of the game.

Big data is changing how tennis stars train and play; but the key to success is taking all that information and turning it into something players can use to win. 

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Craig O’Shannessy, official strategy analyst for both the ATP Tour and all-time great Novak Djokovic, says that the smart use of data when preparing can have a significant impact on a match.

O’Shannessy explains to ZDNet at the ATP Tour Finals in London how he uses a range of tools to give Djokovic that data-led advantage. These tools include the Infosys Tennis Platform, which is being used for the first time in 2019 across the ATP Tour, which is the worldwide top-tier tennis tour for men organised by the Association of Tennis Professionals.

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The platform includes a portal that gives players and coaches access to advanced analytics and match video. This portal uses artificial intelligence and machine learning to match big data with video of game-changing points. Players and coaches have access to more than 100 filters in over 1,000 combinations to analyse performance, and the potential strengths and weaknesses of opponents.

O’Shannessy says the ability to use a data-led portal to create a custom playlist of video clips is “a very big deal”. Being able to easily and quickly highlight the key points in as little as 20 minutes after a match is a potential competitive differentiator.

“When I first started working with Novak, at the 2017 Australian Open, I sat down with him and said, ‘Listen, I do a lot of different things, how can I best help you?’ The first thing he pointed to was video, the ability to see himself play and the ability to see the best patterns of play,” he says.

O’Shannessy also uses tools from Tennis Analytics, a big-data specialist that provides match and technical analysis services to top professionals. He says the smart use of video and analytics technology from a coaching perspective is all about distilling information.

“The worst thing that I could do is show Novak the match chronologically – that’s not the way to do it in today’s game,” he says. “Success is about taking big data and simplifying it and distilling it, so that a player can use it under five minutes with you – whether it’s the video, the numbers or the data tables. It’s about finding the 10 or 15 points that matter the most and explaining that these are the patterns of play that that you want to repeat in upcoming games to win those matches.”

O’Shannessy paints a picture of a sport that is using as much data as possible to help hone player performance. He says the use of technology within tennis has become more entrenched during the past three decades – and he expects the sport to pioneer more data-led developments in the future.

“We need to remember where our sport has come from in order to know also where it’s going to,” he says. “Before 1991, we weren’t counting anything – we didn’t have analytics. Now, I can use technology to focus on player performance. I want to put numbers to everything and that’s the route that I want the technology to continue to take.”

Smart preparation starts a long way from the tennis court. “Every guy that Novak plays, I have video and analytics on, so there are no more surprises coming from the other side of the court,” he says. “So, is it worth one point a match? Absolutely. Is it worth two? For sure it’s worth two points. I’d say in a three-set match, it’s worth more in the range of five to 10 points.”

Other experts also note the willingness of tennis to embrace technology. Chris Brauer, director of innovation in the Institute of Management Studies at Goldsmiths, University of London, says that, while a common theme in his research is a lack of consensus when it comes to change, he says the people running tennis – such as O’Shannessy – are open to innovation.

“He’s able to ask the question of the system and create a set of insights that allows him to be clear about the reality of what’s actually happening on court,” he says. “That’s the difference between a data-driven insight approach to tennis and one that’s based exclusively on intuition or experience – which is hugely valuable, but which on its own can lead us as human beings towards a lot of false positives.”

ATP umpire Ali Nili is another senior figure in the game who believes tennis has taken a pioneering approach to technology. He gives the example of the early implementation of live, online scoring in the sport. Nili also refers to the introduction Hawk-Eye technology 15 years ago, which provides automated line-calling assistance to umpires.

SEE: Wimbledon: How AI, chatbots and big data help serve up a winning experience

While other high-profile sports like football have been slow to use similar systems, tennis has been one of the trailblazing adopters. The more recent and heavy use of data and video to help coaches hone player performance is just the latest in a long line of innovations.

“You see that a lot of major sports are now just trying to adopt Hawk-Eye and use technology,” says Nili. “We’ve been good at that and we hopefully continue to be good at exploring innovations and trying things.”

The experts at the ATP Tour Finals event in London suggested the next area of development in tennis is likely to focus on connectivity, particularly in terms of IoT sensors on balls and on players. These sensors would provide even more data to players, coaches and other potentially interested parties, including broadcasters and fans.

Some sports are already pioneering developments in this area, including golf’s European Tour, as ZDNet found out recently. So, for once, tennis might have to play catch-up when it comes to introducing new technology. However, the sport’s history of innovation leaves Goldsmith’s Brauer thinking that the future of tennis will be closely intertwined with innovation.

“Hardly a day goes by when a new technology isn’t being released that helps to inform this process,” he says. “The coaches and the community of players and stakeholders at the highest levels of the game are entirely engaged. As a result, the game has already been fundamentally transformed by the opportunities that insight and analytics provides – and there’s more to come.”

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Microsoft (NASDAQ:MSFT 148.06 0.51%) has partnered with the UK chipmaker Graphcore to provide cloud-based cutting-edge artificial intelligence solutions that utilize the computing power of specialized chips designed by the British company.

“This [move] is very significant for us because it means anyone on the planet can go to Azure and use our technology to build out their systems,” said Graphcore’s chief executive Nigel Toon. “It’s a massive step for the availability of our technology and a testament to its level of maturity.”

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The Bristol-based $1.5 billion company designs and manufactures specialized chips to run intensive applications that assist machines in face recognition, speech comprehension, parsing of language, autonomous driving, and training of robots. Graphcore’s first commercial product, launched in 2018, was a 16-nanometer PCI Express card known as the C2. It is this product that will launch on the Azure platform, thereby, allowing Microsoft to expand the gamut of its cloud-based services and features for business-critical AI operations.

Technical specification and performance metrics for Graphcore’s chip

The C2 includes two interlinked Colossus IPUs with each containing 16 cores and 23.6 billion transistors. A single chip’s 1,216 IPU can reach over 100 GFLOPS per core (one GFLOP equals one billion floating point operations per second) and is coupled with 300 MB of memory to run up to 10,000 programs in parallel executions. Each chip boasts a memory bandwidth of 45 TB/s resulting in a total bandwidth of 90 TB/s – a theoretical maximum that, according to Graphcore, is 100 times higher than that of HBM2 graphics chip memory.

The C2 is compatible with Graphcore’s modified Poplar, a scalable graph programming framework for AI and machine learning. It boasts an integration with Google’s (NASDAQ:GOOGL 1309.15 1.00%) TensorFlow framework and provides a full training runtime in relation to the Open Neural Network Exchange – an ecosystem for interchangeable AI models. According to Graphcore, a preliminary compatibility with Facebook’s (NASDAQ:FB 193.15 -0.02%) PyTorch is anticipated to arrive in Q4 2019 with full compatibility arriving in early 2020.

The benchmarks published by Graphcore indicate that the C2 matches or exceeds the performance of the top AI chips from Nvidia (NASDAQ:NVDA 209.79 0.58%) and Google using algorithms written specifically for those rival platforms (you will find the full benchmarks here). Note that the code written explicitly for Graphcore’s hardware may be even more efficient. As a reference, the company trained one Bidirectional Encoder Representations from Transformers (BERT) variant – the BERT Base – in just 56 hours with a single IPU server that contained eight C2 cards. Moreover, according to the company’s claims, customers have reported, on average, an inferencing throughput that is 3 times higher along with latency improvements of 20 percent. As far as image recognition is concerned, the European search engine Qwant has claimed a 3.5 times improvement in its image search application since it began to use Graphcore chips.

The deal between Microsoft and Graphcore – a winning formula for Azure

In last December, Garphcore was valued at $1.5 billion through a $200 million funding round with contributions from the likes of Microsoft and BMW (ETR:BMW). For 2019, the company expects $50 million in revenue. The chipmaker employs over 200 engineers and is currently expanding its footprint in Norway, Taiwan, and the U.S.

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The corporate vice-president of Azure Compute at Microsoft, Girish Bablani, said: “The Graphcore offering extends Azure’s capabilities and our efforts here form part of our strategy to ensure that Azure remains the best cloud for AI.”

The AI chip market is currently quite crowded with the involvement of startups such as Cerebras along with tech giants such as Google, Apple (NASDAQ:AAPL 262.64 -0.69%), Amazon (NASDAQ:AMZN 1754.6 0.08%), and Facebook. Interestingly, the Californian start-up Cerebras – a Graphcore competitor – unveiled, in August, the world’s largest chip with a surface area that is slightly larger than a standard iPad in order to power AI applications.

Microsoft’s pre-millennium meteoric rise can be attributed to its partnership with Intel (NASDAQ:INTC 57.81 -0.14%) that resulted in Windows OS running on Intel’s processors. Now Microsoft hopes to dominate the AI age with this newfound partnership with Graphcore.

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We live in a fabulous era of technological achievements. Practically something new, something better emerges every day. But can we catch up? The recent speed of innovation outpaces the capacity of many individuals and teams to adapt. That is why the playing field for business is changing.

Over the past several years things like big data, transformation, digitalisation, artificial intelligence and robotic process automation (RPA) have turned from fantasy into reality. These trends have influenced the future of many business sectors, including the managed services industry.

Once the preserve of books and movies, artificial intelligence (AI) is becoming a reality in today’s world. It is already here in fact – all around us. From big data to our personal devices, from workplaces and manufacturing automation to intelligent home systems and even healthcare – powerful processing units and next-level software algorithms are making our lives easier in an unprecedented ways and pace.

And here comes the question: Should we be afraid of AI?

Artificial intelligence is by definition the simulation of human intelligence processes by machines, especially computer systems. AI systems can learn (acquire information and rules for using it), make reasonable decisions (i.e. they “know” how to use rules to reach conclusions) and self-correct.

On the other hand, “artificial general intelligence” (AGI) systems are those that really disturb the public. These are hypothetical machines that will exhibit behaviour, skills and flexibility as humans do. They will possess generalised human cognitive abilities, creativity, self-consciousness and will be able to find a solution when presented with an unfamiliar task, without human intervention. Futurists are afraid of the so-called “singularity” event – a hypothesis that the invention of artificial superintelligence will abruptly trigger fast self-improvement cycles, resulting in the creation of a system far surpassing the capabilities of human intellect and civilisation. The big problem is that an algorithm could not possibly have emotions and empathy and being superior to humanity will be posing a potential grave threat to the existence of human race in general.

Sounds scary? Naturally, except it does not exist. It is just a theory. There are only couple of dozen organisations in the world involved in research on AGI. Nobody could claim that they are somewhere close to creating an AGI machine. And even if in decades or centuries there happens to be progress in that field, it will be an object of strong regulation.

How about RPA? Do we even need automation?

It is vital for each and every business to find means to grow, improve efficiency levels and be flexible when developing and providing products and services. Among the top 10 challenges for developing companies in 2019 are the integration of new technologies, innovation and customer service improvement. Users have never been more informed and have never had higher requirements when being supported by a company.

RPA is among the fastest-growing industries worldwide due to the solution it provides to some of the major problems faced by large businesses. The process automation via software eliminates the need for some frequently repetitive tasks to be handled manually, thus allowing employees to focus on activities that have higher value and importance. Gartner reports that by the end of 2020 around 40 per cent of large businesses will have integrated RPA software which will be optimizing the workflow within the company. In some industries the percentage of companies that have already implemented RPA is even higher.

Each day innovators in the business create new products, methods and ideas. By aiming to provide services with higher quality and optimisation of the business process, for instance, the Modis team looked for opportunities for higher productivity and effectiveness. The Modis business is related to delivering managed services and solutions, IT service desk and data centre support, support and maintenance of end-user devices, development of applications and solutions for outsourcing of business processes. It turns out that RPA has the potential to satisfy many of the needs of such types of business. That is why in 2017 the Modis Bulgaria team launched the Modis Innovation and Automation (MIA) Lab – a project aiming at developing innovative solutions for automation of tasks, performed by employees.

In response to global trends to meet increasingly complex client needs and maximize efficiency Modis Bulgaria invested in tools, systems and resources, and launched a series of innovation and automation projects and initiatives to further improve its capacity to transform and optimise service and cost of delivery.

The MIA Lab has become one vertical centre of excellence that provides automation solutions across the company in the fields of automation, analytics and workforce management.

The objective is to focus on creating efficiencies in our solutions leveraging latest technologies, by automating basics and repetitive tasks and allow employees to focus on the more interesting and rewarding aspects of their jobs.

With the establishment of the MIA Lab we have put the main focus on removing repetitive work by utilising RPA. The purpose of the lab is to solve critical business problems, create new kinds of smart solutions and add value, using advanced innovative technologies.

The MIA Lab team have managed to complete 18 projects removing repetitive work from service desk personnel, improving the speed and accuracy of transactions as well as service level agreements. By using the latest technologies, we have managed to provide a better customer experience and upskill the service desk personnel, enabling them to focus on added value activities.

What is the future of the IT managed service?

We are living in the era of the so-called Fourth Industrial Revolution, that will see an unprecedented change in the way humans live and work. Many professions will disappear, but that doesn’t actually mean that people will be unemployed. In fact, the same thing has happened in the past. In the early 19th century the Luddites, a group of radical English textile workers, were afraid that the time they spent learning the skills of their craft would go to waste because of the introduction of machines. They even destroyed machinery as a form of protest. Actually, towards the end of the century, it became increasingly apparent that technological progress was benefiting all sections of society, including the working class.

Ideally, implementing machine learning, AI and RPA will create an environment which will enhance the human experience for both the organisation and the employee. Increased efficiencies will allocate more resources for the highest-value interactions. Increasing speed and better quality will come without sacrificing meaningful communication and relationships. That could possibly be the right balance leading to the best possible outcome.

Managed services organisations will need to significantly increase their functional and technical capabilities in these IT segments to better manage their client’s problems and requirements. Transforming its workforce with intensive training and assessments will be instrumental.

The post AI and RPA: Threat or opportunity for the IT managed services industry? appeared first on Artificial Intelligence.