Oracle plans to bring AI to its ERP and CRM cloud applications
In the run up to Computing ‘s inaugural AI and Machine Learning Live! event on November 19, we’ll be rounding up the latest news stories and features in this blog. Pop back every day for new stories on machine learning, robotic process automation, image recognition, natural language processing and more – and why not join us at the event? It promises to be a great day.
24/10/2018 Oracle acquires AI company-intelligence firm DataFox Oracle has acquired DataFox, a SaaS AI firm that crunches large volumes of data on public and private businesses and feeds the results into an AI engine to create company-intelligence that customers can add to their CRM.
The San Francisco-based startup received initial funding from Google Ventures in 2014 and counts Bain Capital, NetApp and Goldman Sachs among its customers. Co-founder and CEO Bastiaan Janmaat was a growth equity analyst at Goldman Sachs before founding the firm, and the investment bank has a stake in DataFox.
In a letter to DataFox customers and partners, Steve Miranda, executive VP applications development at Oracle, said: “The combination of Oracle and DataFox will enhance Oracle Cloud Applications with an extensive set of AI-derived company-level data and signals, enabling customers to reach even better decisions and business outcomes.”
DataFox pulls information on millions of businesses from multiple sources including news articles, digital properties and ‘unique signals’ and analyses them to provide real-time information on when a company’s fortunes might be about to change. Oracle says it plans to “enrich” its cloud applications such as ERP, CX, HCM and SCM with “AI-driven company-level data”. Presumably the idea is to steal a march on cloud compeition from the likes of Salesforce.
Terms of the acquisition have not been disclosed.
19/10/2018 UK supermarkets to trial AI checkouts for age-verification
Facial recognition technology is to be trialled by UK supermarkets for age verification purposes, with a few as-yet-unidentified stores rolling out the scanning tech at self-service checkouts this year and more widely in 2019. The rollout is being led by US vendor NCR, which makes self-service checkout machines for Tesco and Asda among others. The company will integrate an ‘AI-powered camera’ (whatever that may be) into the checkout machines, which will be able to estimate the age of shoppers when they are buying restricted items like cigarettes and alcohol. Read more on this story here.
19/10/2018 AI – where does the liability lie?
The arguments regarding liability in the event of error or incident are beginning to expand. As developments continue, and the use of AI becomes more mainstream, there will increasingly be cases which call in to question who has liability for the systems in use.
So says Emma Stevens, associate solicitor – dispute resolution, at law firm Coffin Mew in this article for Computing .
The majority of the existing legislation and case law in relation to liability and duty in cases of negligence significantly pre-dates the ongoing robotics revolution. It is clear that the legal system has a lot of ground to cover before it can effectively regulate such advances and the existing law will need to be translated to apply to situations where considering the role and impact of AI and robotics was not previously necessary. Businesses would be sensible to make themselves aware of the technological advances in the sectors in which they operate, to ensure that their contracts are clear regarding liability (both generally and for AI) and that they have adequate insurance in place for any systems used, where appropriate.
16/10/2018 It’s big companies that are making the running in machine learning, survey
A survey of data scientists, software engineers, architects and senior management has found that large organisations are taking the lead in their experiments with machine learning, with respondents in large organisations more likely to consider their efforts as ‘sophisticated’ and to have their early successes rewarded by increasing budgets than those in smaller firms.
About half of the respondents were located in the US, a quarter in Asia with the remainder based elsewhere. The survey was conducted by Algorithmia, a US company offering a marketplace for machine-learning models.
Across the entire sample, the main drivers for deploying machine learning models were generating customer insights and intelligence and improving the customer experience. However, in large enterprises improving customer loyalty topped the list, mentioned by 59 per cent. Large enterprises were also more likely to mention cutting costs as being a motivating force.
Just 10 per cent of companies counted themselves as sophisticated in their use of AI and machine learning. The report notes that the sort of companies that pioneered big data techniques a few years back also have a headstart when it comes to deploying machine learning models. They have the data, the infrastructure and the skills required to build proprietary internal platforms – or ‘AI layers’ – on which to deploy. Examples include Facebook’s FB Learner, Netflix’s Notebook Data Platform and Twitter’s BigHead. It seems likely that this lead widen as investment follows success.
A statistic that demonstrates the general immaturity of the field is the fact that 55 per cent of efforts are driven by IT compared with 37 per cent by the business.
12/10/2018 China will overtake the US in AI, predicts former president of Google China Kai-Fu Lee
Kai-Fu Lee, head of VC firm Sinovation Ventures and former president of Google China, says that AI’s influence will be hugely disruptive to everything from the geopolitical power balance to the job market and peoples′ individual feelings of self worth. While some of the changes will be for the better, many will not, he says, warning against the techno-utopianism common in Silicon Valley.
The speed of the coming AI revolution makes parallels with the job creation that accompanied the proliferation of electrical power and the industrial revolution redundant, Lee argued.
“Those earlier technological revolutions took a century or longer,” Lee explained, in a fascinating if discomfiting interview with IEEE Spectrum . “That gave people time to grow, and develop, and invent new jobs. But we have basically one generation with AI, and that’s a lot less time.”
“We’ve opened Pandora’s box,” Lee went on, contrasting AI with other technological threats. “We did, as humans, control the proliferation of nuclear weapons, but that technology was secret and required a huge amount of capital investment. In AI, the algorithms are well known to many people, and it’s not possible to forbid people to use them. College students are using them to start companies.”
Lee believes the fact that the algorithms are easily available means that the nations with the most computing power – and the most centralised command structures – will get make the running, ultimately exporting their innovations to others that might try to slow the tide to cushion its impacts. China has big advantages over current leader the USA, he said, as companies such as Tencent, which has close connections to the Chinese government, have the data, the infrastructure and a workforce that′s quite prepared to get stuck into the more humdrum parts of developing AI.
“Chinese entrepreneurs find areas where there’s enough data and a commercially viable application of AI, and then they work really hard to make the application work. It’s often very hard, dirty, ugly work. The data isn’t handed to you on a silver platter.”
Much of the learning data for the ML algorithms comes from applications like Tencent′s all-encompassing WeChat app, which is “Facebook, Twitter, iMessage, Uber, Expedia, Evite, Instagram, Skype, PayPal, GrubHub, LimeBike, WebMD, Fandango, YouTube, Amazon and eBay” rolled into one.
