These types of situations are what programs such as the MIT-developed Moral Machine have been looking to unlock. The site has for the past four years canvassed public opinion by presenting a series of ethical dilemmas: should an autonomous car protect its passenger for instance, or a pedestrian, in a particular scenario?

The wider point, however, is that such autonomy – or to put it more romantically, machines thinking for themselves – requires vast amounts of data. Data which, to date, has not been calibrated. Would you entrust your safety to your vehicle?

This is an analogy which Dorian Selz, co-founder and CEO of Squirro, uses to explain why the great AI revolution might take longer than many think. “I think we’re going to see massive advances in the underlying technology over the next 10 years – we are probably going to see some level of breakthrough to have some low level of intelligence in these machines,” Selz tells AI News. “I can imagine that – but it will take time until it is really adopted widely.”

For the CEO of an AI platform provider, this may be an interesting position to take – something which Selz accepts. Yet Squirro’s business case may give an indication. The company’s modus operandi is  taking organisations’ unstructured data sets – everything from emails, to market reports, to PowerPoint presentations and Word documents – and utilising what it calls ‘augmented intelligence’ to surface greater insights.

This collaborative process not only leans on the human factor, but takes time to appreciate. “Most companies have ratios of 90% to 99% of all data they generate or acquire never being used beyond the first use,” explains Salz. “That is the equivalent of buying a car, driving it from the dealership back to your garage, and then throwing away the key and never driving that car again.

“If you say data is the new oil, at the same time companies are not making use of that data,” he adds. “What works wonderfully well for numbers doesn’t work at all for text in an Excel.”

Selz knows of which he speaks with regard to how companies use and stockpile data. Squirro is the fourth company he has co-built, with a previous success being Swiss homegrown search engine local.ch. With Squirro, which promises benefits from customer and service insights to cognitive search, the goal is for this data to complement business processes and working methods.

Take an accounts department, for example, whereby the data can be able to show not just whether a client has paid or not, but any relevant news or market research which could impact their ability to pay. In the supply chain, issues can be identified several levels down the chain which may impact the direct supplier.

“If you look at any company today, small or big, their IT landscape is effectively a combination of boxed applicastions – a supply chain system at the back of the company, an ERP system in the middle, some level of CRM at the front and support systems in the background,” explains Selz. “The bigger the company, the more of that stuff you have, but it’s still essentially a landscape of boxed IT applications.

“The future we see is a future where you’re going to see a machine learning-driven layer that weaves all these different data silos together,” Selz adds. “We foresee a future where these types of intimately weaved informational fabrics become the new norm in any enterprise.

“It doesn’t replace you – I don’t believe in that at all. But it will support you in decision making.”

One customer which exemplifies this ‘weaving’ of data is Brookson, a UK-based local accountancy firm for contractors. The company had already been using Squirro to classify data coming in to expedite the administrative and call centre process, but the next step was particularly noteworthy: using the technology to assess future customer relationships. Based on a roadmap created from previous customers, good, bad or indifferent, every new interaction can be assessed and forecast using pre-defined journeys.

“I love to retell that story, because it shows that AI per se is not just for the big boys,” says Selz. “As a nimble, agile, SME enterprise, you can use this type of technology to really fundamentally put yourself in a better position in the marketplace.”

This is an interesting point; plenty of research, not least a study from VC company Work-Bench in 2018, has argued that bigger companies are at a natural advantage with AI because they can hoover up the best talent. But as AI expertise does not automatically equal big businesses, big effects are not the results of such technology yet, Selz argues – going back to his original theme around transformation.

“Everybody talks about these big intelligence machines that do these fantastic things for companies and automate,” he explains. “Transform entire industries? I don’t really believe that.

“If you look at many machine learning techniques in use today, from the more simplistic ones like SVM, Bayesian algorithms, all the way to more sophisticated deep learning models, at the end, they’re not really intelligent,” Selz adds. “It’s pattern matching at the power of whatever computer you have.”

Selz notes that ‘none of the lovely AI engines had foreseen the coronavirus’. Given the reactive, rather than proactive, steps taken by governments worldwide, it wasn’t just the machines who were flat-footed. Looking at various stories assessing AI’s impact on Covid-19, many argue its role is to assess what will happen next – rather, assessing the damage once the horse has bolted – with a Singapore research unit predicting last month, with extreme caution, that the pandemic will end globally by December.

This informs Selz’s overall theory that we are nowhere near ready for primetime in B2B just yet. “It’s outright dangerous to think such an AI engine can suddenly transform your business if it is so limited in its capabilities and capacity,” he says.

“A customer relationship is more than what you have in your data sets,” Selz adds. “Even in retail volume segments, no one has a fully digitised customer relationship yet. If you don’t have it fully digitised, every aspect of it, why would you entrust some anonymous algorithm call method to evaluate and effectively decide on the future of that relationship?

“No sane person would do that.”

Editor’s note: You can find out more about augmented intelligence at AIM, a virtual event hosted by Squirro on June 23-25.

 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, , and Cyber Security & .

The post Dorian Selz, CEO, Squirro: Why the AI revolution will take time appeared first on AI News.

The firm’s first demo day, the company announced earlier this week, gave the accelerator’s inaugural group of startups an opportunity to showcase their projects. These include FaGouGou, a provider of AI-powered legal consultancy services. JD.com says the company is “now playing a critical role in fulfilling the needs arising from a current shortage of experienced lawyers in China.”

With the help of the AI Accelerator program, JD.com has now opened up its broad resources to China’s next-gen AI startups by offering a wide range of support services to help them successfully get off the ground. The startups are also getting access to JD.com’s rich application scenarios across various parts of business which includes legal, retail, HR and logistics.

With an initial roster of 16 startups, the program has already witnessed above 80% of its participants succeeding in introducing their new AI technologies through JD’s businesses.

As the competition to build smart cities in China is picking up the pace, JD.com has also announced the launch of its new smart city initiative. As reported by The Drum, the firm is focussing on expanding its efforts to help Chinese cities become a smart city by integrating its work on AI, big data and IoT into the real economy. In order to do this, JD.com has come up with a new smart city brand named JD iCity.

The brand will be focusing on becoming an industry digitalisation partner and ‘intelligent total solution provider’ for China’s urban development. The firm is keeping a close eye on a number of smart city constructions in China and will also be working with the local governments in Suqian and Nantong, Jiangsu province and Xiongan New Area till the end of 2019 to build more smart cities.

 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, , and Cyber Security & .

The post JD.com’s AI Accelerator enables new startups to push boundaries of real-world AI technology appeared first on AI News.

Staff (left), who has been at M&S for just under 12 months having previously been in the civil service, splits his time between managerial duties and getting his hands dirty. In a field which is so evidently fast-moving, he notes, anyone who spends too much time away from the frontline will be none the wiser for it.

“I set the team up just under a year ago, and so a huge part of my role has been identifying areas of value for us to support in and embedding a data drive culture in the food business,” says Staff. “Despite being in a managerial role, I think it’s absolutely vital to still get stuck in with doing the analysis – the data analysis space is moving so fast with tools and techniques at the moment that it’s easy for managers to fall behind,” he adds, “so this is my way of not doing so.”

So what has Marks & Spencer been doing in this arena? At the beginning of 2018, the retailer announced an ambitious five year ‘technology transformation’ plan, which the company said at the time would be “designed to create a more agile, faster and commercial technology function that will work with the business to deliver growth.” In July, M&S announced more than 1,000 of its employees would be ‘upskilled’ to create the world’s first ‘data academy’ in retail.

For Staff, at the frontline, it’s all about deploying machine learning models to drive greater efficiency and value to the business. “Using machine learning for one-off research purposes is cool and interesting but deploying models or the outputs of models within apps is where it’s at,” he says. “Automating decision making is where the power of data science truly comes into its own and adds organisational value.”

What’s more, it was a driving factor in Staff joining M&S. Despite ‘loving’ working in the civil service and praising its work, the need to not be tied in to long-term projects was key. “The civil service gave me a really [good] understanding of what it meant to work on truly large-scale projects. It’s also doing a lot of great work in the data science and analysis space in terms of modernising,” says Staff.

“At the stage I’m at in my career I wanted to be working at a place where I could truly ‘fail fast’ – be able to test models and technologies quickly and learn from them, and government still has its hands tied in that area in a way I’ve found M&S doesn’t,” he adds.

“However, what I’ve noticed is data quality is a challenge the world over – if anyone tells you that the government data is rubbish compared to the private sector, don’t believe it!”

“Using machine learning for one-off research is cool and interesting – but deploying models or the outputs of models within apps is where it’s at”

This makes for interesting reading compared to when sister publication IoT News spoke with Johan Krebbers, IT CTO at Shell, last year. His view was that quality of data was not so much of a problem as opposed to waiting for perfect data to arrive. “I’m less worried about quality of data because you can never get good quality of data,” he said in June. “You have to use the data you have today, start using it, make it visible, and then start improving.”

Another aspect which was important to Staff when joining M&S was around each party’s expectations. Writing last March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted frustrations in the industry around what employers need and employees want. “The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports,” Brooks-Bartlett wrote. “In contrast the company only wanted a chart that they could present in their board meeting each day.”

Invariably, the end goals of both are wide of the mark. Recruiters, with the aim of smoothing over any issues, can sometimes make things more difficult. Mark Miller, creator of the All Day DevOps online events, recently noticed a job advert which asked for at least five to seven years’ experience of Kubernetes in production. Kubernetes was originally released in July 2015.

Staff is no stranger to this – one recruiter once excitedly told him he would be ‘neural networking’ in a particular placement – but notes these occupational hazards are unfortunately available in most areas.

“In any industry where society suddenly sees the value there’s going to be an explosion in demand,” he says. “Organisations will ‘over-data science’ roles in order to make them sound more appealing to candidates, and candidates will over-sell themselves in order to be snapped up.

“What drew me to M&S is that they didn’t oversell – they sold it on the fact it would be carte blanche and I’d be expected to find the opportunities,” Staff adds. “It was a challenge and a risk definitely, but exciting and full of potential rewards too.”

Staff is speaking at the AI & Big Data Expo in London on April 25-26 alongside Ocado – they of the aforementioned ass kicking – with an intriguing panel session set to focus on self-service big data tools and unlocking unstructured data to create learnable features. Find out more about the event by visiting here.

Picture credit: “Marks And Spencer Department Store – Norwich – England”, by Suzy Hazelwood, used under CC BY-NC 2.0

 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, , and Cyber Security & .

The post Callum Staff, Marks & Spencer: On the need to fail fast, machine learning models and universal data quality challenges appeared first on AI News.

Retail is a major focus of AI but there’s debate over whether it will have a positive or negative on society, especially with regards to jobs. Some believe AI will assist existing jobs while others take a view it will replace workers.

Capgemini found just one percent of retailers have achieved the level of AI deployment needed to reach its full potential.

Kees Jacobs, VP of Global Consumer Products and Retail Sector at Capgemini, said:

“For global retailers, it appears reality has kicked in regarding AI, both in terms of what the technology can achieve and what they need to do to get there.

Of course, deploying and scaling will be the next big objective, but retailers should be wary not to chase ROI figures without also considering the customer experience.”

Most retailers, according to the researchers, are focusing their efforts on using AI for sales and marketing purposes. The company notes AI has the potential to be used across the value chain.

“Our research shows a clear imbalance of organizations prioritizing cost, data and ROI when deploying AI, with only a small minority considering the customer pain points also,” comments Jacobs.

“These two factors need to be given equal weighting if long-term AI growth, with all of the benefits it brings, is to be achieved.”

400 global retailers who’ve implemented AI were studied for the research; accounting for 23 percent of the global retail market by revenue. Public data from the world’s largest 250 retailers by revenue was also included.

Over a quarter (28%) of retailers had deployed AI in 2018, up from just 17 percent in 2017.

As for the job loss fears, 71 percent of the retailers said AI was creating jobs. However, 68 percent of the roles were of a senior level which could be out the reach of lower-skilled retail workers AI may displace.

Currently, 75 percent report AI has not replaced any jobs in their organisation.

The post Capgemini: AI is a $340 billion opportunity for the retail sector appeared first on AI News.