Suddenly it's all about small- and medium-sized businesses (SMEs) as this oft-overlooked sector of the global economy now takes center stage. In the FinTech world, a swathe of products are being launched to suit the sector, be that blockchain-based ideas to speed up invoice payment or access to capital funds that were previously unavailable. Now the AI world is joining in with promises of predicting consumer behavior geared towards SMEs who have to compete with tech giants that mop up the best data scientists and their algorithms. The current state of play means SMEs find it hard to compete with the cost, complexity and consulting taken by big enterprises. So step forward tech company Xineoh that says it has developed a platform for predicting customer behaviour with AI'which allows businesses to out-predict their competition thus allowing them to maximize efficiency and customer satisfaction'. Its so-called bespoke AI solutions on Xineoh's platform can be implemented rapidly without the cost, complexity and consulting required by other methods.

Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today's data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use. Donna Burbank is a recognized industry expert in information management with over 20 years of experience helping organizations enrich their business opportunities through data and information.

On March 29, 2018, a Toyota Land Cruiser carrying five members of the Al Manthari family was travelling through the Yemeni province of Al Bayda, inland from the Gulf of Aden. The family were heading to the city of al-Sawma'ah to pick up a local elder to witness the sale of a plot of land. At two in the afternoon, a rocket from a US Predator drone hit the vehicle, killing three of its passengers. One of the four men killed, Mohamed Saleh al Manthari, had three children aged between one and six. His father, Saleh al Manthari, says Mohamed was the family's only breadwinner.

When a man in the Middle East appears to be performing miracles, a rift breaks out over whether he's the Messiah or a fraud in this suspenseful drama.

Today's blog post is a Q&A session with top fintech influencer and founder of Unconventional Ventures, Theodora Lau. Named one of 44 "2017 Innovators to Watch" by Bank Innovation, ranked No. 2 Top FinTech Influencers 2018 by Onalytica, and named to the list of LinkedIn Top Voices 2017 for Economy and Finance, she's a powerful voice in the industry. If you probe into the rapid adoption of artificial intelligence (AI) initiatives in the enterprise, it quickly becomes clear what's behind it: big data. In a 2018 NewVantage Partners survey of Fortune 1000 executives, 76.5 percent cite the greater proliferation and availability of data is making AI possible. As Randy Bean in an MITSloan Management Review article puts it, "For the first time, large corporations report that they have direct access to meaningful volumes and sources of data that can feed AI algorithms to detect patterns and understand behaviors….these

Wachter told his captive Bay Area audience, "the future of AI and digital transformation will not be created in Mountain View or Cupertino. They simply do not and cannot know enough about the way the healthcare system works. Nor will it be created at UCSF, because we don't have the business expertise or scale." Such transformation, he said, will only occur with partnerships. Beyond academic, enterprise, and startup collaboration, Fernando noted, "markets like China and India, maybe Asia and Africa, are going to come up with new ways to deliver high quality care that we will import into the US--and that might be the biggest disruption." Eron elaborated on this, saying foreign markets have fewer regulations and thus might have access to more, diverse data sets to train algorithms.

Following a challenging 2017, the South African insurance industry must adopt technology and collaborate with local governments, if it is to effectively tap into new markets to stimulate growth for the foreseeable future. Many sectors have been very quick in adopting new technology, like the financial services industry, who have been at the forefront of technological innovation. It is time the insurance sector does the same. Luckily, we have seen certain pockets in the insurance industry follow suit, such as Lemonade, who launched an AI claims bot. It has the potential to investigate and approve a personal lines claim in a record, yes wait for it, 3 seconds!

We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules. In particular, our aim is to provide bounds on the (expected) number of incorrect inferences that are made in this way. Since for classical inference it is in general impossible to bound this number in a non-trivial way, we consider two inference relations that weaken, but remain close in spirit to classical inference.

We compare various extensions of the Bradley-Terry model and a hierarchical Poisson log-linear model in terms of their performance in predicting the outcome of soccer matches (win, draw, or loss). The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations. The prediction performance of the various modeling approaches is assessed using a novel, context-specific framework for temporal validation that is found to deliver accurate estimates of the test error. The direct modeling of outcomes via the various Bradley-Terry extensions and the modeling of match scores using the hierarchical Poisson log-linear model demonstrate similar behavior in terms of predictive performance.

I can't run a starship with twenty crew. KIRK: And what am I supposed to do? WESLEY: You've got a great job, Jim. All you have to do is sit back and let the machine do the work. One clear message from the presidential election is that the dream of good factory jobs still resonates in America's rust belt. Despite the push for students to pursue STEM careers or move into the service sector, Americans still want to make stuff.