The shape-shifting, artificial intelligence (AI)-powered cars seen in futuristic films aren't appearing anytime soon but AI technology certainly exists today in the auto market and will be increasingly visible in the future. Industry observers are saying that AI and machine learning have reached a tipping point and will cause enormous advances through the next few years in many markets. In the automotive sector, these technologies will be transformative. Analysis firm IHS Markit predicted that the installation rate of AI-based systems in new vehicles would rise by 109% in 2025, compared to a modest 8% adoption rate in 2015. In February 2017, Ford paid $1 billion for artificial intelligence start-up Argo AI.

AI in Production – AI is only used by banks in production in a few key use cases such as high-frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

Parascript has introduced a data location, extraction and verification software solution that deploys template-less, neural network-based document extraction. Parascript says it has'productised' it's machine learning platform to support custom-developed recognition projects with much quicker turnaround than traditional rules-based approaches. The result is significantly faster production with more reliable and refined results. "Machine learning offers a whole new set of opportunities for organisations across many industries to more precisely streamline their operations and deliver rapid, accurate data to their clients," said Greg Council, Vice President of Marketing and Product Management. Traditional recognition and capture solutions often successfully use business rules to process information.

Republican Senator Jeff Flake of Arizona revealed on Wednesday that he intends to introduce a resolution to roll back privacy rules that restrict what internet service providers can do with customer data, according to a report from Politico. Flake said he intended to eliminate the Broadband Consumer Privacy Rules by using the Congressional Review Act, which allows Congress to undo rules set by federal agencies with a simple majority vote. The rules that Flake is taking aim at were first passed by the FCC in October 2016 and were aimed at giving consumers more control over their data by requiring internet service providers to ask for permission before using sensitive information. "We like to say it makes [consumers] the king or queen of their information," Dallas Harris, a policy fellow at open internet advocacy group Public Knowledge, told International Business Times. "The way the order does that is by saying, 'Okay internet service provider, if you would like to use sensitive information, you have to ask your consumers first.' The order defines sensitive information as information regarding a user's finances, health, information from children, precise geolocation data, web browsing history, and app usage history. It also includes any unencrypted message content that may be accessible to the service provider. Information deemed to be non-sensitive can be collected by default requires an option to opt-out. Internet service providers oppose the rules and lobbied for the FCC to undo them --a very real possibility under the leadership of Ajit Pai, who was appointed to the head of the FCC by President Donald Trump. Commissioner Pai opposed the rules when they were passed last year, arguing at the time, "if the FCC truly believes that these new rules are necessary to protect consumer privacy, then the government now must move forward to ensure uniform regulation of all companies in the Internet ecosystem at the new baseline the FCC has set." Pai's argument is essentially that if the FCC wants to make handing over consumer data require an opt-in for ISPs, it should for edge providers like Facebook and Google as well. It's unlikely Pai would extend the rules as he suggested, but he could modify them. "The agency is free to change its mind" on the ruling, Harris explained, "It just can't do so in an arbitrary and capricious manner...they can't just willy-nilly with no reason decide to rescind them." Doing so would require opening the rules up to public comment and would require the agency to change the rules based on new facts. Pai and the FCC also have the option to make changes by reviewing petitions for reconsideration, which allows the commission to change a rule without opening the process up to public comment. Both options are preferable to using the Congressional Review Act, which Harris calls "entirely too blunt of an instrument" to handle such a rule. "There really is no benefit to using the CRA here.

Even if they're not quite sure what it is or whether they are already using it. That was the big takeaway from a December study by B2B targeting platform Demandbase, which itself has implemented AI in its platform. And that's a key conclusion from another recent study, conducted by Forrester Consulting for Adgorithms, which has created an AI-driven marketing platform called Albert. The study, "AI: The Next Generation of Marketing," conducted in-depth surveys with 150 marketing executives. "Forrester found that confusion and misunderstanding of AI-driven marketing is quite prevalent today. Indeed, many marketers in our study have a very narrow view of current advanced contextual marketing capabilities, much less around AI-driven marketing tools that can make these contextual programs considerably more efficient and effective, while reducing the complexities marketers face in executing and orchestrating digital interactions. However, when the benefits of AI-driven marketing were proposed to them, they were overwhelmingly likely to find these benefits appealing."

LONDON – Warning: the Kremlin is trying to split the West by spreading "altered facts," conducting blackmail and setting up front organizations, the U.S. State Department said -- in 1981. So-called active measures were common during the Cold War, when the U.S. and the Soviet Union sought to unify and divide Europe with equal urgency. Now those tactics appear to be back, retooled for the digital age as President Vladimir Putin embraces the even older Russian foreign policy tradition of "derzhavnost," or "great powerness." Fears of Russian interference are rampant across the continent. Already reeling from Brexit, the European Union faces a string of key elections starting next month in the Netherlands, then in France and Germany.

AI is not a "platform," It's an enabling technology. Many "X-with-ML" startup business plans (where X is some category of software) …but not so simple. The Ironman Suit 4. Replacing Humans 4 Models (Not Equally Common Today) 7. Model #1: Tell Me Something New Improve customer experience Data: Collect surveys, reviews/social, transactions, call logs, etc. ML: NLP on customer interactions Insight Workflow: What (concretely) makes customers happy? Extract useful data from cheap, frequent satellite images ML: Computer vision to recognize, count, measure, track objects Find use cases: government, finance, oil & gas, etc. Improve construction efficiency Data: Collect timesheets, geo, cost codes, orders, notes, etc. ML: Computer vision to tag images, NLP on notes and orders Insight Worflow: What impacts our productivity? Problem--first: Data--first: 8. Model #1: Tell Me Something New Questions to Consider… Do you have advantaged access to the data?

In our book, AI for Game Developers, we cover many different AI techniques that are used in games. Many of the techniques we cover, such as chasing and evading, pathfinding, finite state machines, and rules-based systems, among others, have obvious applications in games. However, some of the other techniques we cover, such as neural networks, genetic algorithms, and Bayesian techniques, are not as familiar and thus their applications in games may not be as obvious. Nonetheless, these latter techniques offer compelling capabilities when applied in games and they are quickly gaining popularity, as evidenced by their appearances in game development literature, conferences, and indeed the games. Throughout our book we give you multiple code examples and additional ideas of how you can apply all of the techniques we cover in your own games.

This paper presents a knowledge-based approach to the task of learning and identifying galaxies from their images. To this effect, we propose a crowd-sourced pipeline approach that employs two systems - case based and rule based systems. First, the approach extracts morphological features i.e. features describing the structure of the galaxy such as its shape, central characteristics e.g., has a bar or bulge at its center)etc., using computer vision techniques. Then it employs a case based reasoning system and a rule based system to perform the classification task. Our initial results show that this pipeline is effective in learning reasonably accurate models on this complex task.

Knowledge Graph (KG) plays a crucial role in many modern applications. Nevertheless, constructing KG from unstructured text is a challenging problem due to its nature. Consequently, many approaches propose to transform unstructured text to structured text in order to create a KG. Such approaches cannot yet provide reasonable results for mapping an extracted predicate to its identical predicate in another KG. Predicate mapping is an essential procedure because it can reduce the heterogeneity problem and increase searchability over a KG. In this paper, we propose T2KG system, an end-to-end system with keeping such problem into consideration. In the system, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its identical predicate in a KG. Based on preliminary experimental results, the hybrid approach improves the recall by 10.02% and the F-measure by 6.56% without reducing the precision in the predicate mapping task. Furthermore, although the KG creation is conducted in open domains, the system still achieves approximately 50% of F-measure for generating triples in the KG creation task.