Ed and Andrew Frazelle, a father and son team, who are the owners of RightChain, advanced supply chain optimization, planning, and analytics software company based in Atlanta. At the same time, Frazelles are tennis lovers and were intrigued to see if their planning concepts could be applied to the sport.
Ed Frazelle contacted Craig O’shannessy who runs match analysis company Brain Game Tennis, who has been working with Djokovic, among other pros, since 2017. As Loudin points out, “he analyzes their patterns of play and helps them understand both how to improve their own performance as well as which strategies they should employ against specific opponents.”
O’Shounessy’s partner in his work is Warren Pretorius, the CEO of Tennis Analytics, “who developed a model of video analysis that utilizes manual tagging, which he pioneered in 2013.” His method is to chart matches over 25 key indicators and then “combines data analysis and visualization to extract match information and generate keywords on indexed video.”
Frazelle says that he met with O’Shounessy and Pretorius at Wimbledon and that, “we literally started running data that night.” It turned out that RightChain’s AI apps help companies Colgate, Caterpillar, Ford, Baxter, and Coca-Cola simplify their supply chains by breaking the process down into 25 components. Loudin gives an example where forecasting utilizes “an A.I.-based algorithm to craft and continually update a unique model for each product. Network optimization uses an algorithm that determines where to place distribution centers based on a multitude of user-defined criteria.”
To apply his methodology to tennis, Frazelle decided to break down a tennis ball’s journey from end to end in a similar way. As he explains, “For tennis, we changed the fields to focus on the destination and origin of the ball. It’s a very formal coordinate system that maps the tennis court to a level of detail not previously available.” (In this case, each service area is divided into 12 sub-zones, and the backcourt is divided into eight such zones.)
Analytics of just the tennis play by itself is quite one dimensional, and as O’Shounessy explained, the A.I. can find repeatable patterns, measure rally lengths, and determine where precisely a player hit a ball. “The technology offers us extra layers and patterns for a more detailed analysis. It’s one thing to tell a player what’s happening, and another to show them with tables and graphs. The graphs that Ed provides cut the data up to multiple ways and easily lead our eyes to where the real keys of winning live.”
O’Shannessy also said that one of his toughest sells to players has been convincing them that consistency of play — the long rallies that occur in practice — “was overrated, something that video analytics can’t quite prove but A.I. can.”For his part, Pretorius added that “Instead of looking at data in isolation, with A.I., they now can get the story of their play evolution.”
In the end, Novak Djokovic won the 2019 Wimbledon tournament, with O’Shannessy adding that the use of AI is “just the start of where the technology can take the sport.”
Microsoft Partners with Startup Graphcore to Develop AI Chips
Microsoft hopes that its Azure cloud platform will catch up in popularity with Amazon and Google, so as Wired reports, it has partnered with a British startup Graphcore to come up with a new computer chip that would be able to sustain all-new artificial intelligence developments.
As Wired notes, Bristo, UK startup Graphcore “has attracted considerable attention among AI researchers—and several hundred million dollars in investment—on the promise that its chips will accelerate the computations required to make AI work.” Since its inception in 2016, this is the first time that the company is publicly coming up with its chips and testing results.
Microsoft’s invested in Graphcore in December 2018 “as a part of a $200 million funding round”, as it wants to stimulate the use of its cloud services to a growing number of customers that use AI applications.
Graphcore itself designed its chips from scratch “to support the calculations that help machines to recognize faces, understand speech, parse language, drive cars, and train robots.” The company expects that its chips will be used by “companies running business-critical operations on AI, such as self-driving car startups, trading firms, and operations that process large quantities of video and audio, as well as those working on next-generation AI algorithms.”
According to the benchmarks published by Microsoft and Graphcore on November 13, 2019, “the chip matches or exceeds the performance of the top AI chips from Nvidia and Google using algorithms written for those rival platforms. Code is written specifically for Graphcore’s hardware maybe even more efficient.”
The two companies also stated that “certain image-processing tasks work many times faster on Graphcore’s chips,” and that “ they were able to train a popular AI model for language processing, called BERT, at rates matching those of any other existing hardware.”
Moor Insights AI chip specialist Karl Freund is of the opinion that the results of the new chip show that it is “cutting-edge but still flexible,” and that “they’ve done a good job making it programmable,” an extremely hard thing to do.
Wired further adds that Nigel Toon, co-founder, and CEO of Graphcore, says the companies began working together a year after his company’s launch, through Microsoft Research Cambridge in the UK. He also told the publication that his company’s chips are especially well-suited to tasks that involve very large AI models or temporal data. Also, one customer in finance supposedly saw a 26-fold performance boost in an algorithm used to analyze market data thanks to Graphcore’s hardware.
Some other, smaller companies used this occasion to announce that “they are working with Graphcore chips through Azure.” This includes Citadel, which will use the chips to analyze financial data, and Qwant, a European search engine that wants the hardware to run an image-recognition algorithm known as ResNext.
Startups Creating Tools To Monitor AI and Promote Ethical AI Usage
Over the course of the past year, it seems that more and more attention is being paid to ensuring that AI is used in ethical ways. Google and Microsoft have both recently warned investors that misuse of AI algorithms or poorly designed AI algorithms presents ethical and legal risks. Meanwhile, the state of California has just decided to pass a bill that bans the use of face recognition technology by California’s law enforcement agencies.
Recently, startups such as Arthur have been attempting to design tools that will help AI engineers quantify and qualify how their machine learning models perform. As reported by Wired, Arthur is trying to give AI developers a toolkit that will make it easier for them to discover problems when designing financial applications, like unveiling bias in investment or lending decisions.
Arthur’s efforts are aimed at addressing the “black box” problem of AI. The black box problem in AI describes how unlike traditional code, which can be easily interpreted by those who know how to read it, machine learning systems map features to behavior without unveiling the reasons that these behaviors are selected/how the features have been interpreted. In other words, in a black box system the exact implementation of the algorithm is opaque.
Machine learning systems operate by extracting patterns from input data and reasoning about these patterns. This is accomplished by essentially having a computer write its own code by manipulating certain mathematical functions. In order to address this problem, researchers and engineers need tools that make the observation and analysis of machine learning software behavior easier. Startups like Arthur acknowledge the difficulty of solving this problem and don’t claim to have the optimal solutions, but they are hoping to make progress in this area and make cracking open the black box a little easier. Its hoped that if AI systems can be analyzed easier, it will become easier to correct problems like bias as well.
Large companies like Facebook already have some tools to analyze the inner workings of machine learning systems. For example, Facebook has a tool dubbed Fairness Flow which is intended to make sure the ads that recommend jobs to people target people from all different backgrounds. However, it is likely that large AI teams won’t want to invest time in creating such tools, and therefore a business opportunity exists for companies that want to create monitoring tools for use by AI companies.
Arthur is focused on creating tools that enable companies to better maintain and monitor AI systems after the system has already been deployed. Arthur’s tools are intended to let companies see how their system’s performance shifts over time, which would theoretically let companies pick up on potential manifestations of bias. If a company’s loan recommendation software starts excluding certain groups of customers, a flag could be set that indicates the system needs review in order to ensure it isn’t discriminating against customers based on sensitive attributes like race or gender.
However, Arthur isn’t the only company creating tools that let AI companies review the performance of their algorithms. Many startups are investing in the creation of tools to fight bias and ensure that AI algorithms are being used ethically. Weights & Biases is another startup creating tools to help machine learning engineers analyze potential problems with their network. Toyota has used the tools created by Weights & Biases to monitor their machine learning devices as they train. Meanwhile, the startup Fiddler is working to create a different set of AI monitoring tools. IBM has even created its own monitoring service called OpenScale.
Liz O’Sullivan, one of the co-creators of Arthur, explained that the interest in creating tools to help solve the Black Box problem is driven by a growing awareness of the power of AI.
“People are starting to realize how powerful these systems can be, and that they need to take advantage of the benefits in a way that is responsible,” O’Sullivan said.
London-Based Startup LabGenius Raises $10M
The London-based startup LabGenius announced that they raised over $10 million in Series A Funding. They are a drug discovery company that utilizes artificial intelligence (AI), robotic automation, and synthetic biology. Their main focus is to find novel protein therapeutics.
According to Dr James Field, CEO and Founder of LabGenius, “Protein therapeutics have an unparalleled potential to both treat disease and alleviate human suffering. By transforming how these drugs are discovered, we have a shot at improving the lives of countless people. Being able to robustly engineer novel therapeutic proteins has immense commercial and societal value. The discovery of protein therapeutics has historically been highly artisanal, relying heavily on humans for both experimental design and execution. This dependence has proved limiting because, as a species, we’re cognitively incapable of fully grasping the complexity of biological systems.”
The Series A investment round is led by Lux Capital and Obvious Ventures. Other participants included Felicis Ventures, Inovia Capital, Air Street Capital, and other existing investors. CEO and founder of Recursion Pharmaceuticals, Chris Gibson, along with Inovia Capital General Partner Patrick Pichette, are also investing. Pichette is the former CFO of Google.
According to the company, they will use the capital to “scale their team, expand the scope of its discovery platform, and initiate an internal asset development program.” One of their main goals is to evolve novel antibody fragments. These could be used to treat certain conditions that can’t rely on conventional antibody formats.
Lux Capital and Obvious Ventures
Zavain Dar, Partner at Lux Capital, along with Nan Li, Managing Director at Obvious Ventures, have been involved in the life science startup environment for some time. Their investment strategy dates back nine years, including a 2013 investment into Zymergen, a molecule discovery and manufacture company based out of California. In 2016, they were involved in Recursion Pharmaceuticals, who went on to a series C raise of $156 million in July.
Their strategy follows a path, starting at industrial biotech technology with Zymergen and followed by root-cause biology discovery with Recursion Pharmaceuticals. It is closed out by creating composition of matter and IP with LabGenius.
Dar explained his reasoning behind choosing LabGenius over other startups.
“We scoured the globe, and didn’t want to be constrained by what happened to be in our backyard,” he says. “They are leading the pack…and now with backing and pharma partnerships, should be in a good position.”
Humans No Longer Sole Agents of Innovation
When speaking to TechCrunch, Dr James Field said, “My central thesis, the thing that’s really the driving force behind the company, is the conviction that we’re entering an age in which humans will no longer be the sole agents of innovation. Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines. An empirical computation engine is an artificial system capable of recursively and intelligently searching a solution space.”
The company has created a discovery platform called EVA, and it integrates multiple new technologies coming from different fields including artificial intelligence. After discovery and characterisation, LabGenius then sends their proprietary molecules to clinics.
Field explains the company’s EVA technology as a “machine learning-driven, robotic platform”,” that is capable of “designing, conducting and critically learning from its own experiments.”
“For decades, scientists, engineers and technologists have dreamt of building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies and sophisticated real-world products,” says Field.
“For protein engineers, that dream has now entered the realm of possibility. The rapid pace of technological development across the fields of synthetic biology, robotic automation and ML has given us access to all the essential ingredients required to create a smart robotic platform capable of intelligently discovering novel therapeutic proteins.”
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