Online portals like Slashgear and others have brought the news that tech giant Sony is launching a new global division which will have its focus on developing artificial intelligence, The goal is to apply MA through gaming, photography and even cooking. It is planned that the new division will have offices throughout the world. As reported, Hiroaki Kitano, President, and CEO of Sony Computer Science Laboratories will be the global head of Sony AI. Peter Stone will be the US chief, a computer science researcher with a storied background in Reinforcement Learning and Robotics.
The news coincides with Sony’s official press release about the research agreement the company signed with Carnegie Mellon University (CMU) on joint work to develop AI and robotics. Their initial efforts will focus on cooking and delivery.
Already trying to strengthen their AI-development efforts, Sony has brought back Aibo robotic dog with the new version set to take “full advantage of things like cloud processing, facial recognition, and learning AI’s to make the pup more realistic and engaging.” While Aibo development on the surface might seem like “ fairly playful interpretation of artificial intelligence,” Sony’s purpose behind the toy’s development is to explore “how such technologies can ‘unleash human imagination and creativity with AI,’ tapping into all of the company’s different divisions in the process. It’ll also look beyond Sony’s existing businesses, to figure out new potential applications.”
Using this as a stepping stone, Sony’s teams in Japan, the US, and Europe plan to launch “three flagship projects’” one of which is the aforementioned gastronomy research with CMU. The other two planned fields are gaming and imaging&sensing.
Sony points out that it is focusing on gaming as games are widely used in AI experiments, and “and game worlds are a commonly implemented research method to develop artificial intelligence.’ Of course, there is a more direct application on Sony products as the new AI teams could “help the PlayStation team develop smarter in-game characters.”
For imaging and sensing, Sony aims to produce new types of sensors that can be used as “sense organs” for artificial intelligence. “In that case, we can consider AI systems in a wide variety of scenarios, from real-time data analysis to robots, to agents within a server space. Each of those scenarios has different requirements in terms of processing time or level of input necessary.” In that field, as Endgadget notes, Sony has already made strides in products like the A6600 camera, where it contributes to an impressive improvement in autofocus.
Discussing its plans to develop the AI-use in gastronomy, Sony says that it intends to “harness AI and robotics from the perspective of “getting closer to creators,” and with the aim of expanding the creative options/creativity of chefs. Top-level chefs can gain inspiration for new recipes by interacting with AI, and we also want to help all of those who prepare food in their day to day lives to enjoy that process of creation even more.”
Appen’s State of AI Annual Report Reveals Significant Industry Growth
Appen Limited (ASX: APX), the leading provider of high-quality training data for organizations that build effective AI systems at scale, today announced its annual State of AI Report for 2020.
The State of AI 2020 report is the output of a cross-industry, large-organization study of senior business leaders and technologists. The survey intended to examine and identify the main characteristics of the expanding AI and machine learning landscape by gathering responses from AI decision-makers.
There were multiple key takeaways:
- While nearly 3 out of 4 organizations said AI is critical to their business, nearly half feel their organization is behind in their AI journey.
- AI Budgets greater than $5M doubled YoY
- An increasing number of enterprises are getting behind responsible AI as a component to business success, but only 25% of companies said unbiased AI is mission-critical.
- 3 out of 4 organizations report updating their AI models at least quarterly, signifying a focus on the model’s life after deployment.
- The gap between business leaders and technologists continues, despite their alignment being instrumental in building a strong AI infrastructure.
- Despite turbulent times, more than two-thirds of respondents do not expect any negative impact from COVID-19 on their AI strategies.
One of the key findings is that nearly half of those who responded feel their company is behind in their AI journey, this suggests a critical gap exists between the strategic need and the ability to execute.
Lack of data and data management was reported as a main challenge, this includes training data which is foundational of AI and ML model deployments, so, unsurprisingly, 93% of companies report that high-quality training data is important to successful AI.
Organizations also reported using 25% more data types (text, image, video, audio, etc.) in 2020, compared to 2019. Not only are models getting more frequent updates, but teams are using increasingly more data types, and that will translate in an increasing need for investment in reliable training data.
One key indicator of exponential growth of AI was the rapid YoY growth in AI initiates. In 2019, only 39% of executives owned AI initiatives. In 2020, executive ownership of AI skyrocketed to 71%. With this increase in executive ownership, the number of organizations reporting budgets greater than $5M also doubled.
Global cloud providers gained significant traction as data science and ML tools compared to 2019. This may be due to increased budget and executive oversight. What is even more impressive is the increase of respondents who are reporting using global cloud machine learning providers which are identified as: Microsoft Azure (49%), Google Cloud (36%), IBM Watson (31%), AWS (25%), and Salesforce Einstein (17%). Each of these front runners saw double-digit adoption increases vs 2019, proving that as more companies are moving to scale, they’re looking for solutions that can scale with them.
To learn more, we recommend downloading the entire State of AI and Machine Learning Report.
AI Drug Company Exscientia Raises $60 Million in Series C Funding
The British AI drug development company Exscientia has raised $60 million in a Series C financing round. It was led by Denmark’s Novo Holdings, which is a new investor, along with existing investors Evotec, Bristol Myers Squibb, and GT Healthcare Capital.
The funding round comes as there is a growing interest in AI-driven drug discovery, and it will help the company expand internationally, with a special focus on a presence in the United States.
Prof. Andrew Hopkins is CEO and founder of Exscientia.
“This investment highlights the increasing commitment to the potential of AI to transform drug discovery and the excitement that we have garnered around the innovative work we are doing at Exscientia,” said Hopkins. “We have now demonstrated multiple times that our platform can accelerate the time between the start of an idea and a viable new drug candidate for patients in need of treatments, fast tracking the entire R&D process.”
Robert Ghenchev is Senior Partner and Head of Novo Growth, which is the growth equity arm of Novo Holdings.
“Through its impressive track record to date, Exscientia has demonstrated the value of combining deep scientific expertise with cutting-edge technology capabilities to significantly accelerate drug discovery,” Ghenchev said. “Novo Holdings’ investment underscores our interest in supporting technology companies that enable life science research and innovation, and our commitment to this area. We see significant opportunity for the company to further grow its participation in the drug discovery ecosystem and are excited to work with the Exscientia team in realising this vision.”
Dr. Werner Lanthaler is the Chief Executive Officer of Evotec.
“Evotec was the first strategic and operational investor in Exscientia and we are extremely pleased it has made such impressive progress since that time and to be part of this significant fund raising. Evotec and Exscientia, together, look forward to realising further synergistic potential in innovative drug discovery,” Lanthaler said.
The company raised $26 million, 18 months back, from investors including Bristol Myers Squibb and Evotec.
What is Exscientia?
Exscientia is based in Oxford, and the company uses artificial intelligence for small molecule drug discovery. Earlier this year, the company partnered with Sumitomo Dainippon Pharma (DSP) to develop the first-ever, precision-engineered drug designed using AI. The drug serves as a treatment for obsessive-compulsive disorder (OCD), and it entered Phase 1 human clinical trials. The entire project lasted less than 12 months, in a process that traditionally takes 4.5 years with conventional methods.
Since then, Exscientia has forged multi-project partnerships with Bristol Myers Squibb, Bayer, Rallybio, and GT Apeiron.
The company has also been involved in developing treatments for COVID-19. Throughout the entire world, pharmaceutical companies and biotech industries have partnered up to focus on developing a vaccine and treatment for the virus.
Back in March, Exscientia announced that the company was collaborating with Diamond Light Source, a science facility based in Oxfordshire, as well as with drug-developer Calibr, in order to come up with antiviral treatments for COVID-19.
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.
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