Energy – AI News

Google pledges to no longer build AIs for the fossil fuel industry

Lead Editor — Fri, 22 May 2020 15:45:52 +0000

Google has pledged to no longer build AIs for the fossil fuel industry as it further distances itself from controversial developments.

A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent.

In an interview with CUBE’s John Furrier, the leader of Google’s CTO office, Will Grannis, said that Google will “no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations.”

The pledge from Google Cloud is welcome, but it must be taken in a wider context.

In 2019, Google Cloud’s revenue from oil and gas was approximately $65 million. A hefty sum, but less than one percent of all Google Cloud revenues. Furthermore, Google Cloud’s revenue from oil and gas decreased by about 11 percent despite overall revenue growing by 53 percent.

While Google Cloud’s revenue from the oil and gas industry was declining, the public’s intolerance towards big polluters is increasing. The reputational damage caused to Google of continuing its relationship with polluters would likely have been more costly over the long-term.

This isn’t the first time Google has cut-off an AI-related relationship with a controversial industry to preserve its reputation.

Back in 2018, Google was forced into ending a contract with the Pentagon called Project Maven to build AI technologies for drones. Over 4,000 Google employees signed a petition demanding their management cease the project and never again “build warfare technology.”

Following the Project Maven backlash, Google CEO Sundar Pichai promised in a blog post the company will not develop technologies or weapons that cause harm, or anything which can be used for surveillance violating “internationally accepted norms” or “widely accepted principles of international law and human rights”.

Back in January, Pichai called for sensible AI regulation that does not limit the potential societal benefits.

PAX, a Dutch NGO, ranked Google among the safest companies developing AI while slamming rivals such as Amazon and Microsoft for being among the “highest risk” tech firms in the world.

(Photo by Zbynek Burival on Unsplash)

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Q&A: Anton Fedotov, Airband Technologies: On how AI is tackling air pollution

Lead Editor — Thu, 23 Apr 2020 10:03:39 +0000

The ongoing Covid-19 pandemic has been described, in the words of one executive in the air pollution space, as ‘the biggest single global intervention you will ever see.’ Cars almost entirely remain in their garages; commercial aeroplanes lay dormant.

When the crisis ends, it will in some ways be a statistician’s dream. The amount of data which will be able to be extrapolated will be huge, for both scientists and businesses working in this field.

One such business is Airband Technologies. The UK- based company urges businesses, consumers and governments to ‘take the cleaner path’ through a smart, wearable air sensor. The sensor aims to help monitor air pollution and use intelligent routing with real-time data.

AI News caught up with Anton Fedotov (left), co-founder of Airband Technologies, to discuss his company’s vision for cleaner air, as well as the artificial intelligence (AI) technologies being employed to make it happen.

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AI News: Hi Anton. Tell me about your career to date and role and responsibilities at Airband?

Anton Fedotov: I began Airband with a group of co-founders a little over a year ago. We began with an idea, and have spent the year working hard on developing it into something real and physical. Throughout that time, I have been leading the process as CEO of the company – I am involved both in the development of our product, as well as the development of our business, including reaching out and finding prospective investors and clients.

AI: How did the concept for Airband come about?

AF: Our lead engineer was doing research on a project about air pollution, which led him to realise the sheer lack of data which is out there – there is almost nothing aside from a few stationary monitoring sites spread out across larger city. Even data which can be purchased comes from modelling the spread starting from a relatively small dataset. Seeing the development of IoT, crowdsourced, and wearable technologies was our inspiration for a wearable air sensor, which can contribute to a massive granular dataset and help solve the pollution problem.

AI: Tell us about Airband’s wearable air pollution monitor and how AI technologies are being used for it?

AF: Our wearable pollution monitor is a revolutionary device which measures the quality of the air around the user, rather than a fixed space. It provides personalised pollution readings for the area and for the day to people, and it helps employers keep track of and manage their employees’ exposure for high-risk jobs. Airband makes use of AI to maximise sensor potential and extrapolate data, as well as maintaining calibration across the network. Finally, our predictive AQI technology uses AI to predict air quality in gaps of the network, making it the most detailed map of air pollution ever.

AI: What are some of the partnerships Airband is putting together with other companies in this space and how important are partnerships in achieving your goals?

AF: Airband is always on the lookout for partnerships, which will be fundamental to achieving our goals. We are looking at partnerships with service and hardware providers in the IoT sphere to help us develop and deploy both our product and future products across industries. We also are looking for partners in the data science sphere in order to maximise the potential of the data we collect, and help complement our dataset with existing monitoring solutions.

AI: What other initiatives are interesting to you in the green tech space and why?

AF: There are many amazing initiatives in the green tech space which are going on at the moment – some companies are aiming to directly reduce pollution by creating air “Scrubbers” which could serve to clean up the air around busy streets. Other companies are working on making plastics recyclable or even entirely bio-degradable. Perhaps one of the most interesting angles is the companies bringing easier choices to environmentally minded consumers, by providing either information or alternatives when buying reusable products or travelling.

AI: What can we expect from Airband (on the basis of business as usual) for the next 12 months and beyond?

AF: In the next 12 months, Airband hopes to deliver to initial business clients for businesses interested in protecting their employees. The development of our product and data network will be ongoing, and we should be able to start generating meaningful data, showcasing the utility of our product. Over the next 2-3 years, we hope to also expand into the consumer sector, and make the wearable air quality sensor available for all, at an affordable price.

Photo by Thomas Millot on Unsplash

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US Department of Energy to invest $40m in AI and machine learning research

Lead Editor — Mon, 23 Mar 2020 08:15:31 +0000

The US Department of Energy (DOE) has decided to provide funds up to $40 million over a three-year period for new research in data, artificial intelligence (AI), and machine learning to address the challenges associated with issues related to data production and management at DOE scientific user facilities.

Dr. Chris Fall, Director of DOE’s Office of Science, said: “Major scientific facilities at our DOE national laboratories are generating vast and growing amounts of data for researchers every day. Artificial intelligence and machine learning hold out new promise for managing this wealth of data as well as improving facility operations and aiding in experimental design.”

Proposals are likely to cover a wide variety of different challenges, including extracting information from complex data sets, managing facility operations in real-time, and optimising experiments through the creation of virtual laboratory environments, among other topics. The funding opportunity focuses on 18 DOE Office of Science user facilities, comprising of particle accelerators, accelerator test facilities, x-ray light sources, neutron scattering sources, and nanoscale science research centres, overseen by three major programme offices: basic energy sciences, high energy physics, and nuclear physics.

According to the latest Ericsson Mobility Report, data volumes in mobile networks are increasing at an exceptional rate and mobile data traffic is expected to grow fourfold by 2025, reaching up to 160 exabytes per month. This seems interesting and in fact offers all sorts of opportunities for communications service providers; however, there is a potential disadvantage of this rapidly increasing data traffic due to the impact on energy consumption and carbon footprint of mobile networks. But AI has the ability to solve this problem, Ericsson notes, as when deployed, communications service providers will be able to realise energy efficiencies on the radio network proactively. The technology does not just address site-related energy savings, but also operational efficiencies.

Photo by Bonnie Kittle on Unsplash

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Infosys: On how edge computing and blockchain will be key in different ways for AI

Lead Editor — Fri, 07 Jun 2019 10:39:55 +0000

Artificial intelligence (AI) has gone beyond the way of the buzzword, all hype and no substance. Indeed, the technology is being increasingly seen in the enterprise as important in concert with other technologies such as the Internet of Things (IoT) and edge computing.

A report from KPMG last month explored how artificial intelligence would look to influence business. Among millennial respondents, AI came out on top as the biggest technological priority, ahead of IoT and 5G. Yet among those polled who said they were business leaders, it was less of a priority compared with robotic process automation (RPA). The report noted, however, that investing in RPA can be seen as a smoother route to future AI investment.

IT giant Infosys offers many opportunities for companies looking to invest in artificial intelligence to improve workplace productivity as well as enhance the customer experience. The company notes that customers are at different stages of their journeys depending on sector and use case; and that therefore a more nuanced approach is required.

Ahead of the AI & Big Data Expo event in Amsterdam later this month, AI News caught up with Dr. N. R. Srinivasa Raghavan, Chief Data Scientist, Data and Analytics at Infosys (left), to discuss the initiatives the company is putting together, as well as the industries set to benefit most long-term.

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AI News: Tell us about the initiatives Infosys is putting together in the realm of artificial intelligence – and what have customers been saying?

NR: Infosys is developing several industry-oriented AI solutions, frameworks, and workbenches that can help solve business problems. Specifically, AI is being positioned for enhancing the productivity of day to day operations of our clients through automation, enable decision making at tactical and operational levels, and for better customer experience.

Our customers are at different stages of embracing AI. Depending on their needs, Infosys is able to craft solutions driven by a global team of experts in data sciences and AI, and help deploy these solutions at enterprise scale.

AI: Do you agree that many companies/vendors are using ‘AI-washing’ (much in the same way as cloud-washing years ago) and overplaying their artificial intelligence capabilities? If so – what does this mean for users and the industry at large?

NR: We believe it is not as easy to be deceptive in AI as in cloud. AI is more of an outcome heavy tech than cloud, which is more of an infrastructure play. Therefore, one can verify if indeed pattern matching and predictive science of AI is behind any software. Also, the expertise of resources working on AI projects is discernible in the quality of the output.

Nonetheless, there is possibly AI-washing in some very narrow areas like automation. It calls for better validations and governance to be put in place to weed these out.

AI: Which industries do you think are going to benefit most long-term from these kinds of technologies? How important is it that several emerging technologies – artificial intelligence, edge computing, blockchain – can all talk to each other and make each other better to provide greater business outcomes?

NR: Banking, financial services and insurance (BFSI), retail, consumer product goods and logistics (RCL), and services, utilities, resources and energy (SURE), in that order.

While AI and edge computing need to work in synchronization for cases like real time predictions, integration with blockchain will be essential in the data layers of AI. Especially where the reliability, traceability and ‘sovereignty’ of data that is feeding AI is concerned.

AI: What is the most exciting use case you have seen with artificial intelligence to date? (can be business or consumer)

NR: AI for consumer experience, AI in risk/fraud detection, AI for predictive maintenance, AI for security and AI for business functions like HR, Finance etc. These will typically be cases where there is rich and reliable data available for the AI models to work upon, as well as areas where there is executive ownership and sponsorship.

AI: What advice would you give to companies looking to embark upon or modify their digital transformation initiatives?

NR: AI is largely seen as the harbinger for an automated/human-augmented workplace. It is therefore essential to have AI experts to be part of the blue printing for the next-gen digital enterprises. AI cannot be telescoped into digital transformation programs in large enterprises. It is no more a commodity tech, but a strategic one.

AI: What are you looking forward to most at the AI & Big Data Expo and what will you be looking to tell attendees while there?

NR: We are looking forward to hearing from other participants on their success and learnings in implementing AI within Big Data context. We are eager to share our own experience in pushing the frontiers for AI and its adoption within enterprise settings.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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New funding for AI startups in devices, energy and healthcare unveiled

Lead Editor — Tue, 20 Nov 2018 13:04:17 +0000

Plenty of funding is currently swirling around in the field of artificial intelligence across various sectors; take the £84 million announced earlier this month by the UK government around AI, robotics research and smart energy innovation, as well as $2 billion committed by DARPA. Three of the latest funding round announcements showcase a wide range of solutions, from energy to healthcare.

US-based AI semiconductor manufacturer Syntiant raised $25 million (£19.5m) in its latest series B funding round that was led by Microsoft Corp.’s venture fund: M12. Strategic investors like Amazon Alexa Fund, Applied Ventures, Intel Capital, Motorola Solutions Venture Capital and Robert Bosch Venture Capital were also part of this fundraiser. At this event, Syntiant also announced that it is expanding its board to five members by appointing Samir Kumar, managing director of M12, and Bret Johnsen, CFO of Space Exploration Technologies (SpaceX). He will serve as an independent director.

Syntiant is developing semiconductor solutions designed from the ground up for deep learning inference. Syntiant’s neural decision processors (NDPs) use an analogue neural network offers orders of magnitude lower power by extreme memory efficiency along with massively parallel computation with modest precision. This is done without the constraints of legacy processor architectures. The company enables always-on deep learning inference in battery-powered devices that are ideal for applications like hearing aids and IoT, smart speakers and mobile phones, etc.

Canadian language translation services provider Knowtions Research also raised $5 million (£3.9m) in series A funding round led by Information Venture Partners, with participation from Alibaba Entrepreneurs Fund. Knowtions intends to help health insurers in a time when the rapidly increasing healthcare costs are threatening to make insurance policies less affordable for people. The company wants to reverse this trend and is working towards transformation of such health insurers with the power of AI.

Knowtions has developed an AI platform, called Lydia, which helps health insurers unlock and use predictive insights in unusable health data. It learns how people seek medical care globally to make predictions on fraudulent behaviour and health risks. Insurers can use these predictions to create AI-assisted workflows that automate claims processing and personalise customer experience.

Grid4C, an Israeli developer of AI and machine learning solutions for the energy industry, has also raised a $5 million led by ICV — a venture capital firm focused on industrial technology, backed by French energy giant ENGIE and other leading utilities in Europe and in Asia. iAngels and AxessVentures were also among the list of investors.

The company is working with the leading utility companies across the world, delivering billions of predictions for millions of smart meters every day. The company’s analytics solutions leverage the ability of AI and data science to offer utilities with granular predictions and actionable insights for their operations and customer-facing applications.

Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and >.

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