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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 facesunderstand speechparse languagedrive 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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Former diplomat and translator for the UN, currently freelance journalist/writer/researcher, focusing on modern technology, artificial intelligence, and modern culture.


Beyond Limits Raises $133M Series C Investment to Drive Global Expansion of AI Technology




Beyond Limits, an industrial and enterprise-grade AI technology company built for the most demanding sectors, including energy, utilities and healthcare, today announced a milestone Series C funding round with $113 million closed and another approximately $20 million committed. This round is led by Group 42, a prominent AI and cloud computing company, and bp ventures, an existing two-time investor and customer of the company.

“Today we are seeing unprecedented, world-wide demand for systems that go beyond the limitations of conventional AI,” said AJ Abdallat, CEO and Founder of Beyond Limits. “Our cognitive software has the ability to understand situations and place problems in real-world contexts as well as to learn over time. We’re excited to help more customers by applying our unique and powerful AI approach to solve some of the toughest problems facing industries and the world today.”

Beyond Limits is an industrial and enterprise-grade artificial intelligence company built for the most demanding sectors including energy, utilities, and healthcare. Founded in 2014, Beyond Limits leverages a significant investment portfolio of advanced technology developed at Caltech’s Jet Propulsion Laboratory for NASA space missions.

Beyond traditional artificial intelligence, Beyond Limits’ unique Cognitive AI technology combines numeric techniques like machine learning with knowledge-based reasoning to produce actionable intelligence.

“bp ventures was established to identify and invest in high-potential, game-changing technology companies that can help us reimagine our global energy system,” said Morag Watson, Senior Vice President, Digital Science and Engineering at bp. “With this additional investment, we believe that Beyond Limits’ Cognitive AI could help create a more intelligent and sustainable future for the energy sector and indeed across industry as a whole.”

“We look forward to working together and exploring the many capabilities of this advanced technology,” said Martin Edelman, General Counsel, Group 42. “We believe Beyond Limits’ unique AI will bring new levels of efficiency to high-impact sectors and help drive future economic growth.”

In a recent exclusive interview with AJ Abdallat, AJ discussed how leverages cognitive technologies used in unmanned space missions. The Cognitive AI uses human-like reasoning and knowledge provided by domain experts, not just data, to understand situations, solve problems and recommend actions.

The $133M funding will be used to expand Beyond Limits’ business both in the United States and abroad, including the launch of Beyond Limits Asia, with regional headquarters in Singapore and operations in Hong Kong, Taipei and Tokyo and further expansion across Europe, the Middle East, Africa and Asia. The funding will also accelerate Beyond Limits’ Cognitive AI application development and SaaS product portfolio and fuel the Beyond Labs R&D program.

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What are the main obstacles that are preventing AI startups from scaling up? – Thought Leaders




By Salvatore Minetti, CEO, Fountech.Ventures

The promise of artificial intelligence (AI) has undoubtedly captured the imagination of many investors over the past decade. Fuelled by strong public interest, the technology has become a real force for good, promising to deliver solutions with potential to solve some of the world’s biggest issues.

Relative to other emerging technologies, AI companies were the leading investment category globally in 2019, securing over $23 billion in financing according to Tech Nation.

However, AI companies require more than just investment to truly thrive in the current climate. Indeed, the issue is not so much the shortage of start-ups as it is the shortage of scale-ups.

To truly push this discipline forward, it is time that we ramp up our efforts to nurture only the most innovative businesses towards long-term success, so that they can become formidable companies. This begs the question: what are the obstacles holding AI businesses back from growing beyond the start-up phase?

Determining ‘true’ AI businesses

It is no secret that the tag ‘AI’ has become ubiquitous, with companies using the term left, right and centre in order to secure investment. The problem with this is that some companies without AI at their core are holding back progress in the sector at large, hindering the development of progressive solutions.

These issues with semantics make it more difficult for investors to determine which businesses actually use ‘true’ AI, and which don’t. Indeed, a recent MMC Ventures report revealed that two fifths of Europe’s AI start-ups don’t actually use AI in any of their products. Examples like this serve to highlight how pervasive the misuse of the term is. Undoubtedly, conflating the meaning of a product or service can not only lead to overspending and poor execution, but also a business’ ultimate downfall when it is outcompeted by those with more clarity and focus.

Investors would therefore do well to avoid this fate by vetting companies thoroughly early on in the process. This can be achieved by asking key questions, such as ‘does this company derive its competitive advantage from the use of AI?’, and ‘will this company propel the sector forward?’. This way, resource can be spent more valuably on companies with scalable technical solutions and real competitive edge.

Start-up stumbling blocks

In the deep-tech arena, ambitious young teams generally have the determination and technical expertise required to design and create an innovative product. However, powerful concepts aren’t always enough to guarantee the success of a new business venture, and too much focus on the technology could stymie its progress.

The lack of clear metrics for AI startups is particularly challenging; it is difficult to measure what makes a ‘good’ AI company. The hype surrounding AI and its growing popularity has also given rise to fierce competition, which means that founders need to be particularly attuned to the obstacles they will face.

Some fundamentals are important for every business. For one, entrepreneurs must be able to demonstrate that they are addressing a large and important problem – and show why they are in the best position to solve it. Perhaps even more importantly, businesses need to establish whether people will be willing to pay good money for their solution.

AI start-ups will generally fall at many of the same hurdles as their more traditional counterparts. Another CB Insights report revealed the most common reasons that budding entrepreneurs might fail on their way up to the top, which included a lack of market need for the product, not having the right team, and being out-competed by other businesses.

The first of these demands particular attention: the blight of so many tech startups is that they build the product, and then hope that somebody wants it. A failure to take the appropriate steps at the outset to understand the potential fit and demand means that the final product doesn’t ultimately capture the attention of the target market.

For AI businesses, however, there are additional elements that must also be considered. The team should be able to demonstrate that their AI is truly adding value to the data they are using – and not just being used as a smokescreen. Does the AI help explain patterns in the data, derive accurate explanations, identify important trends and ultimately optimize the use of the information?

If not, they must question whether they should really be selling themselves as an AI startup. There is a real risk that resources will be spent needlessly on building and marketing a solution that does not truly solve a problem using artificial intelligence. Ultimately, such businesses are likely to lose their vision over time and will fail to live up to the mark they might have envisaged for themselves. They may also struggle to secure funding; after all, most VCs will not want to risk an investment into a technology that is ambiguous.

Young teams also tend to face roadblocks when it comes to the financial side of things: AI start-ups are either under-funded from the outset or burn more cash than necessary. To achieve sustainable growth, fledgling companies need to be able to plan beyond the development budget and create a scalable commercial model that will stand the test of time. Granted, this is no easy feat with limited business nous.

Nurturing AI start-ups to success

Many of these missteps boil down to the fact that start-ups often fall short where appropriate mentorship and business acumen are concerned. Indeed, most would benefit from some additional expertise to navigate common stumbling blocks.

It is fundamental therefore that company founders work with third-party advisors to compensate for any gaps in knowledge. Young teams need mentors to help manoeuvre unfamiliar territory, and to provide additional legal, financial, and logistical guidance.

Ultimately, simply financing a project just isn’t enough. It is essential that we work to provide a more holistic model to support fledgling AI start-ups, so that companies are set on the path to commercially scalable projects. It is only by providing specialist support and assistance with the more fundamental aspects of business – as well as access to talent, capital and peer networks – that we can really push the needle forward in pioneering AI technology.

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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.

Something of which AI developers may want to take note of is the variability in languages used to build models has also shifted from 2019. While Python remains the most used language in both 2019 and 2020, SQL and R were the second and third most commonly used language in 2019. However, in 2020, Java, C/C++, and JavaScript gained significant traction. Python, R, and SQL are often indicative of the pilot stage, while Java, C/C++, and JavaScript are more production stage languages.

To learn more, we recommend downloading the entire State of AI and Machine Learning Report.

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