Arm shakes up the IP game

Arm has announced the launch of its flexible licensing model to allow customers to access to its IP without breaking their bank accounts.

It’s a model which has the potential to shift traditional dynamics in the segment as Arm aims to shift its customer base outside its traditional mobile market. With the connected era promising a ridiculous number of devices there are riches available for those who can prove their IP is suitable for this varied plethora. This seems to be the strategy in mind.

In short, customers pay a ‘modest’ fee upfront and then negotiate contracts when the team is moving towards production phase.

“By converging unlimited design access with no up-front licensing commitment, we are empowering existing partners and new market players to address new growth opportunities in IoT, machine learning, self-driving cars and 5G,” said Rene Haas, President of the Intellectual Property Group at Arm.

As it stands, Arm works like many other IP businesses. Customers pay the full-amount for access to licences and agree royalty payments, depending on the potential scale of the devices, upfront. Although this is the traditional way in which business is conducted, it is risky as it is an expenditure irrelevant as to whether the Arm IP is used in production or not.

The Arm Flexible Access model effectively delays payment. SoC design teams will be able to engage Arm and its IP before any licences or royalty payments are agreed. In short, customers will only pay for what they use when they get to production, paying only a trial fee at the beginning of the process.

Arm has said the Flexible Access portfolio includes all the essential Intellectual Property (IP) and tools needed for an SoC design. Prototypes can be designed and evaluated in numerous ways before any significant financial commitments are made. Theoretically, it should offer customers more opportunity to experiment without the fear of irreversibly-expensive mistakes or assumptions.

There are now three ways to work with Arm:

Arm DesignStart Arm Flexible Access Standard Licensing
Cost $0 for Cortex-M0, M1 and M3$75k for Cortex-A5 $75k entry package annual access fee$200k standard package annual access fee Upfront license fees based on license terms
Licensing Simple license agreement for DesignStart Pro Sign one-time access and manufacturing agreements Agreement terms vary to cover single or multiple uses
Support Community-based support Standard support and maintenance for all included products Standard support and maintenance for licensed products
Portfolio Click here Click here Access to the most advanced Arm IPLocked-down system-on-chip (SoC) roadmaps with multiple uses of specific Arm IP products

“We are working on several products to address AI use cases in automotive, IoT gateways and edge computing,” said Nagendra Nagaraja, CEO of AlphaICs, an AI start-up. “For this, we need access to a wide range of IP and the ability to rapidly evaluate, prototype and design. Arm’s Flexible Access model gives us that agile approach to IP for the first time.”

This is where the model can be incredibly beneficial for both the ecosystem and Arm. Companies like AlphaICs would have struggled financially to scale under the traditional IP model, it is a 50-strong start-up exploring an embryonic segment of the technology industry. In paying a modest amount up-front, AlphaICs has the opportunity to prove the business case before making any significant financial commitments.

This approach obviously helps the start-ups who are exploring unproven ideas, but it also gains Arm traction in currently unprofitable segments which could scale extraordinarily quickly. AlphaICs is aiming to create the next-generation of AI compute for autonomous edge and data centre applications, not a traditional stomping ground of Arm, but there are certainly growth opportunities.

Cybersecurity is becoming impossible without AI – Capgemini report

Security is certainly a topic which is top of the agenda for almost everyone in the technology world, but it is quickly becoming apparent it will be impossible without AI.

The concept of 100% secure has now been rightfully banished and now more people are waking up to the idea any form of security is going to be impossible without artificial intelligence.

According to a new report from the Capgemini Research Institute, 69% of respondents believe they will not be able to respond to cyberattacks without the use of AI. Such is the velocity, volume and variety of threats thrown towards businesses nowadays, there will never be enough budget or hours in the day for humans to effectively deal with the problem in its entirety.

“Organizations are facing an unparalleled volume and complexity of cyber threats and have woken up to the importance of AI as the first line of defence,” said Geert van der Linden, Cybersecurity Business Lead at Capgemini Group.

“As cybersecurity analysts are overwhelmed, close to a quarter of them declaring they are not able to successfully investigate all identified incidents, it is critical for organizations to increase investment and focus on the business benefits that AI can bring in terms of bolstering their cybersecurity.”

The report follows another interesting bit of research from enterprise ISP Beaming earlier in the week. Beaming suggested the number of attacks levelled at British businesses during Q2 increased 179% year-on-year. These firms were effectively facing a threat every 50 seconds on average over the three-month period.

The Capgemini research suggests investments in security AI will increase dramatically over the next twelve months. During 2020, 48% of decision makers suggested investments in this area will increase by a third. 73% are currently testing use cases for AI in cybersecurity, while 63% intend to deploy AI security in 2020 to bolster defences.

And while it might seem like a grave conversation to have right now, the situation is only going to become worse. With the introduction of 5G, more products and services moving to the cloud, consumers adopting more connected devices and IOT set to boom over the next couple of years, the perimeter is expanding. Threats exist today, but exposure to the dark corners of the web is going to become much more apparent.

Europe publishes stance on AI ethics, but don’t expect much

The European Commission has revealed its latest white paper detailing guidelines on an ethical and trustworthy approach to AI, but whether it actually means anything remains to be seen.

The guidelines themselves are now open for public comment with the Gaggle of Red Tapers seeking feedback on how to make improvements and increase applicability to the world of today. However, the industry continues to operate under the semblance of oversight but in the reality of the digital wild-west.

Such is the top-line nature of the guidelines, you have to wonder whether there have been any real efforts to integrate the thinking into business. At the moment, the guidelines do not seem to have any substance to them, simply stating the obvious, or at least what you would hope is obvious to the developers creating the algorithms and applications. These guidelines would have been useful 2-3 years ago, but now it seems a bit of a redundant statement. AI regulation needs action not philosophical thinking.

After reading the guidelines, there is a sense of ‘so what’. What was the point in making this statement aside from cosmetically attracting headlines for the European Commission? There doesn’t seem to be anything new in there, just the European Commission making a statement for the sake of making a statement.

The seven guidelines are as follows:

  1. Humans should have oversight of AI at all times
  2. AI systems need to be resilient and secure
  3. Governance measures should be introduced to protect privacy
  4. Transparency should be ensured
  5. Bias should be removed
  6. AI should benefit all
  7. Accountability for AI should be introduced

Having the guidelines is all well-and-good, there needs to be a yard-stick, but we would expect at the least for some sort of accountability model. It seems a bit half-arsed at the moment as there are still numerous questions.

Firstly, how is the European Commission going to judge whether these guidelines are being followed by industry? What will the metrics be? What will be the punishments for not taking the principles into account or negligible behaviour? Where are the reporting mechanisms for ‘unethical’ behaviour and complaints?

The next steps for the Commission is to consult with industry and run various pilot programmes across the bloc. After these initiatives have been completed, another consultation period will be entered into before the Commission will review the assessment lists for the key requirements in early 2020. At some point in the ill-defined future, Europe might have some rules on AI.

Considering the posturing which has taken place over the last couple of months, Europe has promised it will lead the world on AI, this announced seems nothing but superficial. These generic comments and guidelines should have been put out years ago, now is a time for action and a time for rules.

AI is already in the world and having a fundamental impact on our day-to-day lives. We might not realise it all the time, but it is increasingly interwoven into the services and products which we use each day. Now is the time for action from regulators, not posturing and pondering.

The threat of Amazon is forcing supermarkets into drastic changes

UK supermarket giant Tesco is undergoing trials with Israeli AI surveillance business Trigo Vision to trial the concept of cashier-less stores, supposedly due to pressure from Amazon.

When Amazon first emerged, few could have imagined the revolution which would have been thrust upon the retailing industry. Even now, more than two decades after Amazon was founded, there are businesses which are still struggling to adapt to life in the digital epoch. The writing has been on the wall for a long-time in retail, and now it seems Tesco is attempting to get ahead of trends for the supermarket segment.

According to The Telegraph, Tesco is currently in trials with Trigo Vision to create a cashier-less store, a concept which Amazon has been playing with in the US for years. Don’t be fooled by the absurdity of the vision, it will soon enough be a presence on the High Street and once the benefits can be seen by all, it will become much more common place.

Take self-checkout tills as an example. When these first emerged everyone hated them, and in some regions, they still do. But you cannot walk into a Tesco or Sainsbury’s in an urban environment anymore without seeing them. And most importantly, no-one really cares anymore. The idea took some time to bed in, but once the bugs were worked out and people saw how much more efficient the system was, they accepted it. The same trend will most likely occur with cashier-less stores.

What is worth noting is that Tesco is not alone in pursuing the future. Sainsbury’s has also announced it is toying with the cashier-less idea, opening its first store in Holborn, Central London, in April this year.

Trigo Vision, the provider of the underlying technology, was founded in 2017 and has been through one round of funding thus far, attracting $7 million from Hetz Ventures and Vertex Ventures Israel. The team already has a partnership with Shufersal, Israel’s largest supermarket chain, to roll-out its automated retail platform in over 272 stores across the country.

The firm supplies both high-resolution RGB cameras, installed on ceilings and an on-premises processing unit that runs machine learning-powered tracking software. The algorithms are continuously honed by Trigo Vision through the data collected at various sites and the team can also help develop customisable apps and kiosks to improve experience.

The technology makes use of artificial intelligence and a dense series of surveillance cameras to track what items are being placed into a customer’s shopping trolley. Customers will be prompted to download an app and enter payment details, or an alternative for sceptics could be using a screen at the exit to complete the purchases.

As it stands, Amazon Go, the eCommerce’s cashier-less business, has launched in several cities across the US and has plans to open its first store in the UK at Oxford Circus in London. This will act as the flagship store for the UK though Amazon is reportedly on the hunt for more sites, 3,000 to 5,000 square feet in size, to expand the footprint.

The stores have been hailed as a success in the US and Amazon is reportedly targeting 3,000 locations within three years. Although this is far from proof the idea is profitable right now, the internet giants tend to run with unprofitable ideas they know will change the world, it should be viewed as a massive red flag for traditional supermarkets.

And while the bookstore segment did little until it was too late, Tesco is at least attempting to get ahead of trends. Another example of this is the ‘Scan Pay Go’ initiative. Here, customers can download an app and carry around a scanner to register products themselves as they wander the aisles, helping keep an eye on spending while also speeding up the check-out process at the end of the trip.

Many companies will state they want to disrupt themselves before being disrupted, though there is little evidence of this. The majority of the time there is an outside influence, a threat from a new player, to alter the status quo. This seems to be the case here, as Amazon is forcing the hand of Tesco, though future success of the Amazon Go business will depend on the ability of the traditional players to scale quickly.

Using machine learning as a stethoscope for 5G

Telecoms.com periodically invites third parties to share their views on the industry’s most pressing issues. In this article Yuval Stein, AVP Technologies at TEOCO looks at the use of machine learning to optimise the roll out of 5G

The invention of the stethoscope was thanks to shyness rather than a spark of genius. In 1816, French doctor René Laennec, felt that listening to a young woman’s heart by pressing his ear to her chest wasn’t appropriate. Instead, he rolled up some paper, and found that he was able to hear much better.

The earliest designs of stethoscopes were simple wooden tubes and it was many years before the instrument we see as emblematic of healthcare was created. But the stethoscope was more than just a useful tool, it led to a new way of doing medicine. Before, it was normal to treat symptoms rather than underlying causes—now, through this new device, doctors had insight into what was going on inside the body, and were better able to understand the diseases behind the symptoms.

This shift from treating symptoms to treating causes seems natural to us now, but at this time doctors would treat a fever with no real idea of what was causing it. A similar change is now necessary with mobile networks. Increased complexity means that we need a new way of looking at these networks, to find the root causes of faults, rather than only treating the symptoms.

Machine learning as a stethoscope

The sheer amount of data that a 5G network will produce is going to be overwhelming. More data is good, of course, because the more data we have on a network, the better we can understand the issues that may be causing problems for users. But all this data needs to be analysed. In the past, this was simple—more data meant hiring more people to analyse and formulate actionable conclusions from the data.

This is no longer tenable. But it’s also no longer possible to rely on simple forms of automation in order to react to regular and more obvious issues. 5G is different from previous network generations, in that many new technologies and architectural innovations are being introduced at the same time. These technologies include NFV/SDN, edge computing, new radio access technologies and more.

This new complexity means we need new tools to examine the network. And this is where machine learning becomes just like the stethoscope—not just a tool, but a shift in how things are done. The use of machine learning can identify patterns and reduce the need for human oversight—a vital means of increasing operational efficiency by reducing headcount. But the real change is shifting from fixing issues to detecting underlying issues—even those that don’t linger in the network for long.

Treating the causes, not the symptoms

The rise of virtualised, software-driven networks has meant that service assurance is more decentralised. This means more network alarms—even with automation it’s still often impossible to determine where the real problems reside. This is particularly an issue where faults are intermittent—the symptoms may last for far longer that the fault itself. Manually examining service alarms will give an engineer no real clue as to where they can start to fix the underlying problems.

Also, there is big difference between being reactive and being proactive in maintaining a level of network assurance. A simple example would be if network bandwidth was too low to provide a certain service, and an alarm is set for when this happens. Automation would mean that the fix for this happens without any intervention. But a step further would be to use statistical techniques, such as trend analysis and forecasting to detect abnormalities in the network. These tools would mean pre-empting a situation that would result in poor service in anticipation of a glitch,  rather than reacting when the issue actually arises. This isn’t about fixing problems, but preventing them before they ever happen, addressing the underlying symptoms before they have a chance to take root and cause havoc.

But machine learning can go further. Self-learning algorithms mean that operators can create a baseline profile that identifies when exceptions occur. Rather than determining a threshold for an alarm, this allows for the creation of adaptive thresholds. An example of this would be an area where many homes are being built—at some point there will be a lot more traffic in that area, but engineers don’t have the time to check how closely construction timelines are being followed. Instead, the network behavior should change to meet the demand automatically. While a hard-coded threshold would need to be reconfigured, machine learning means that thresholds are adjusted automatically.

These examples seem fairly straightforward—but millions of similar decisions need to be made every day based on an overwhelming amount of data. Operators have known that automation is necessary for some time, but machine learning is key to decision-making in a 5G network. Without it, operators will be reduced to guesswork, lacking the tools to make the most out of their new—and expensive—networks.

 

Yuval-SteinYuval Stein is the AVP of Product Management and Service Assurance Products at TEOCO. With more than 15 years of experience in the service assurance domain, Yuval has held key product management positions throughout his career. He brings his knowledge to the fault, performance and service domains, and uses his hands-on experience to adapt service assurance solutions to the industry challenges: digital services and network technologies.

HMD moves Nokia phone user data storage to Finland

HMD Global, the maker of Nokia-branded smartphones, announced that it is moving the storage of user data to Google Cloud servers located in Finland, to ease concerns about data security.

The phone maker announced the move in the context of its new partnership with CGI, a consulting firm that specialises in data collection and analytics, and Google Cloud, which will provide HMD Global with its machine learning technologies. The new models, Nokia 4.2, Nokia 3.2 and the Nokia 2.2, will be the first ones to have the user data stored in the Google Cloud servers in Hamina, southern Finland. Older models that will be eligible for upgrading to Android Q will move the storage to Finland at the upgrade, expected to take place from late 2019 to early 2020. HMD Global commits to two years’ OS upgrades and three years’ security upgrades to its products.

HMD Global claims the move will support its target to be the first Android OEMs to bring OS updates to its users, and to improve its compliance with European security measures and legislation, including GDPR. “We want to remain open and transparent about how we collect and store device activation data and want to ensure people understand why and how it improves their phone experience,” said Juho Sarvikas, HMD Global’s Chief Product Officer. “This change aims to further reinforce our promise to our fans for a pure, secure and up to date Android, with an emphasis on security and privacy through our data servers in Finland.”

Sarvikas denied to the Finnish news outlet Ilta-Sanomat that the move was a direct response to privacy concerns triggered by the controversy earlier this year when Nokia-branded phones sold in Norway were sending activation data to servers in China. At that time HMD Global told Telecoms.com that user data of phones purchased outside of China is stored in AWS servers in Singapore, which, the company said, “follows very strict privacy laws.” However, according to GDPR, to take user data outside of the EU, the company would have had to obtain explicit consent from its EU-based users.

Sarvikas claimed that the latest decision to move storage to Finland has been a year in the making and is part of the company’s overall cloud service vendor swap from Amazon to Google. “Staying true to our Finnish heritage, we’ve decided to partner with CGI and Google Cloud platform for our growing data storage needs and increasing investment in our European home,” Sarvikas added in the press release.

Francisco Jeronimo, Associate VP at IDC, saw this move a positive action by HMD Global, calling it a good move “to address concerns about data privacy” on Twitter.

UK government thinks AI sector isn’t diverse enough

A few million quid is being thrown at artificial intelligence and data science conversion degrees, with people from underrepresented groups eligible for scholarships.

The Department for Digital, Culture, Media and Sport had a rummage around the back of the sofa and came up with £13.5 million that is wants to spend on encouraging people to take ‘conversion degrees’ specialising in AI and data science. This seems to mean you already need a degree to qualify, but have decided the smart money is in AI.

The social engineering doesn’t stop there, however, with a significant chunk of that cash set aside to provide 1,000 scholarships for ‘under-represented groups… including women and people from minority ethnic backgrounds, or lower socio-economic backgrounds.’ Another 5 million quid is being set aside to encourage companies to come up with better online learning tools.

“Creating a more diverse future workforce will help with the design of new technology, including the fair and accurate development of algorithms, and tackle some of the greatest social challenges of our time – from protecting our environment, to transforming the way we live and work, and saving lives through diagnosing diseases earlier,” the announcement says.

“The UK has a long standing reputation for innovation, world-leading academic institutions and a business friendly environment and everyone, regardless of their background, should have the opportunity to build a successful career in our world-leading tech sector,” said Digital Secretary Jeremy Wright. “Through these new AI and Data Conversion courses and our modern Industrial Strategy, we are committed to working with the tech sector and academia to develop and maintain the best AI workforce in the world.”

Loads of other people were quoted as saying almost exactly the same thing. Try as it might the government couldn’t find a single voice of dissent. Apparently only 19 percent of UK tech workers are women, which is bad. No data was offered regarding ethnicity or socio-economic grouping. This initiative joins a Byzantine web of other schemes the government has in place to help people acquire some useful vocational skills. The first round of funding will be made available in September.

Microsoft and Sony join up on AI and cloud gaming

Microsoft and Sony have signed a memorandum of understanding to jointly develop cloud systems for game and content streaming, and to integrate Microsoft’s AI with Sony’s image sensors.

This is another step on Sony’s journey to transform from a console and title seller to a game streaming service platform. Microsoft’s leadership in both cloud computing, its Azure cloud platform, and the global footsteps of its datacentres makes it an ideal partner to Sony.

The collaboration will also cover semiconductors and AI. Sony has been a leader in image sensors (among its clients is the iPhone including the latest XS Max model), and the integration of Microsoft Azure AI will help improve both the imaging processing in the cloud and on device, what the companies called “a hybrid manner”. Microsoft’s AI will also be incorporated in Sony’s other consumer products to “provide highly intuitive and user-friendly AI experiences”, the companies said.

“Sony has always been a leader in both entertainment and technology, and the collaboration we announced today builds on this history of innovation,” said Satya Nadella, CEO of Microsoft, in a statement. “Our partnership brings the power of Azure and Azure AI to Sony to deliver new gaming and entertainment experiences for customers.”

Kenichiro Yoshida, president and CEO of Sony agreed. “I hope that in the areas of semiconductors and AI, leveraging each company’s cutting-edge technology in a mutually complementary way will lead to the creation of new value for society,” he said.

Looking to the future of the PlayStation platform, Yoshida said, “Our mission is to seamlessly evolve this platform as one that continues to deliver the best and most immersive entertainment experiences, together with a cloud environment that ensures the best possible experience, anytime, anywhere.”

Gaming is following the trend of video and music from one-off ownership selling to access streaming. But gamers are more sensitive to the visual quality and, above everything else, lagging. So to provide good experience to convert gamers to long-term streaming subscribers, the platform needs to guarantee superb connection. This is where Microsoft’s datacentre footsteps and the upcoming 5G networks will fit well with the “game” plan.

Another key success factor, similar to video streaming market, is the content. Gamers’ taste can be fast changing and frivolous. That is why the companies also stressed the importance to “collaborate closely with a multitude of content creators that capture the imagination of people around the world, and through our cutting-edge technology, we provide the tools to bring their dreams and vision to reality.”

No information on the size of investment or the number of staff involved in the collaboration is disclosed, but the companies promised to “share additional information when available”.

LG muscles in on competitive AI chip space

LG has unveiled has developed its own artificial intelligence chip in an attempt to muscle in on this increasingly competitive segment of the semiconductor market.

The AI market is proving to be rewarding for those who can prove their worth, and each day there seems to be a new ‘thought leader’ entering the fray. While there is a feeling AI could benefit application developers (Uber, Cruise, Waymo etc.) and internet companies (Amazon, Google, Microsoft etc.) more than the semiconductor giants, there will be winners and losers in this segment also.

“Our AI C​hip is designed to provide optimized artificial intelligence solutions for future LG products,” said IP Park, CTO of LG Electronics. “This will further enhance the three key pillars of our artificial intelligence strategy – evolve, connect and open – and provide customers with an improved experience for a better life.”

Nvidia might have made a run at this segment in the early days, though considering its experience lies in gaming applications, whether it can mount a serious challenge remains to be seen. Graphcore is one which has attracted investment from the likes of Dell, Microsoft and Samsung, while AMD, Intel, Huawei, Google and Qualcomm (as well as numerous others) are making this a very competitive space.

As with Intel in the PC-era and Qualcomm’s continued dominance in mobile, some might suspect there might be a clear leader in AI also.

LG has stated its chip will feature its proprietary LG Neural Engine to better mimic the neural network of the human brain. The aim is to distinguish space, location, objects and users, while hoping to improve the capabilities of the device by detecting physical and chemical changes in the environment. As with every AI plug, LG is also promoting the ability of on-device processing power.

Looking at the approach from LG, the team are targeting quite a niche aspect of the AI segment; the smart home. This makes sense, as while LG has a smartphone business, the brand is perhaps primarily known for its home appliances range.

During the last earnings call, the LG mobile business continued to struggle in a sluggish and cut-throat market, reporting a 29% year-on-year drop to $1.34 billion, though the home appliance market soared. Revenues and profits soared to record levels, accounting for more than 80% of the total profits for the business over the three months.

Future products, such as washing machines, refrigerators, and air conditioners will be fitted with the devices, as ‘intelligence’ and personalisation become more common themes in more generic and everyday products.

Maybe the smart toilet isn’t that far away after all.

UK Gov names members of AI Council

The UK government will be hoping its AI advisory board is a bit more successful than Google’s as it names the full line-up.

Bringing together experts from industry, academia and data rights organisations, the ambition is to provide a guiding light for the future of artificial intelligence. Tabitha Goldstaub, co-founder of CognitionX, will chair the council which will feature the likes of Ocado CTO Paul Clarke, Kriti Sharma, the founder of AI for Good and Deepmind’s co-founder Mustafa Suleyman.

The primary objective of the council will be to make the UK a leading name in the AI world.

Such is the promise of the technology in terms of productivity and the creation of new services, technologists will be keen to drive innovation forward, though the dangers are also high.

AI not only presents the risk of abuse through prejudice and unconscious bias, but the unknown risks should be considered as much of a danger. Such is the embryotic nature of AI, the full-potential, power and influence are anyone’s guess for the moment. This is an exciting prospect, but also should be approached with caution.

For example, back in July 2017, a Facebook AI application managed to invent its own language to speak to other applications meaning human overseers had no idea what was going on. This was a very simplistic and limited application so there was no real danger, but it was a lesson to the industry; more defined perimeters need to be created for more complex applications in the real world.

This council will aim to create a framework to take the UK into a leadership position in the AI world, but it will be critical the members do not forget about the importance of ethical and responsible development.

“Britain is already a leading authority in AI,” said Secretary of State for Digital, Culture, Media and Sport, Jeremy Wright. “We are home to some of the world’s finest academic institutions, landing record levels of investment to the sector and attracting the best global tech talent, but we must not be complacent.

“Through our AI Council we will continue this momentum by leveraging the knowledge of experts from a range of sectors to provide leadership on the best use and adoption of artificial intelligence across the economy.”

The full list of members:

  • Tabitha Goldstaub, Chair and Cofounder of Cognition X
  • Wendy Hall, Professor of Computer Science at the University of Southampton
  • Professor Adrian Smith. Institute Director and Chief Executive at the Alan Turing Institute
  • Alice Bentinck, Co-founder at Entrepreneur First
  • Alice Webb, Director for Children’s and Education at the BBC
  • Ann Cairns, Executive Vice Chair of Mastercard
  • Professor Chris Bishop, Microsoft Technical Fellow and Director of the Microsoft Research Lab in Cambridge
  • Dr Claire Craig, Chief Science Policy Officer at the Royal Society
  • Professor David Lane, Professor & Founding Director of the Edinburgh Centre for Robotics
  • Kriti Sharma, Founder of AI for Good
  • Marc Warner, CEO of Faculty
  • Professor Maire O’Neill, Professor at Queen’s University Belfast
  • Sir Mark Walport, Chief Executive of UKRI
  • Martin Tisne, Managing Director of Luminate
  • Mustafa Suleyman, Co-founder of Deepmind
  • Professor Neil Lawrence, Professor at the University of Sheffield and Director, IPC Machine Learning at Amazon
  • Professor Nick Jennings, Vice-Provost Research and Enterprise of Imperial College
  • Dame Patricia Hodgson, Member of the Independent Commission on Freedom of Information and Centre for Data Ethics and Innovation
  • Paul Clarke, CTO of Ocado
  • Professor Pete Burnap, Professor of Data Science & Cybersecurity at Cardiff University
  • Priya Lakhani, Founder of edtech AI platform Century Tech
  • Rachel Dunscombe, CEO of NHS Digital Academy