The challenge is insight: online store managers find it much harder to see what's really going on in the shop, compared to their real world counterparts. However new analytical techniques, powered by AI technologies, are helping businesses optimise their UX and improve their bottom lines in new and important ways. Combining these with advanced user journey mapping can provide essential insight to inform marketers as to why people are dropping out of the site at certain points, while next-generation element'zoning' of key elements on a certain page can give employees and much more micro and detailed overview of page performance (such as revenue generated or hesitation rate per'zone') at a glance. In the coming years businesses will find it progressively easier to eliminate intuition from the product and marketing development cycle through a powerful combination of UX analytics and AI-driven automated recommendations.
The artificial intelligence technology is deployed by cybersecurity firms in an effort to keep pace with the evolution of cyberattacks, as machine learning algorithms are able to improve predictability the more it is used. But according to Guy Caspi, CEO of cybersecurity company Deep Instinct, machine learning is no longer enough in an age of unprecedented evolution and volume of cybercrime. Part of that is because machine learning relies on only two or three algorithms; deep learning deploys tens of algorithms, and complex math. But the ongoing evolution of corporate cybercrime means cybersecurity companies may no longer be able to afford relying solely on machine learning.
Do we intend to verify or test the system only for "naturally occurring" legitimate inputs, or do we intend to provide guarantees for its behavior on arbitrary, degenerate inputs? Statistical learning theory provides guarantees that the test error rate is unlikely to exceed some threshold, but these guarantees are often so conservative that they are not used by engineers in practice. The natural way to test robustness to adversarial examples is simply to evaluate the accuracy of the model on a test set that has been adversarially perturbed to create adversarial examples [SZS13]. It is clear that testing of naturally occurring inputs is sufficient for traditional machine learning applications, but verification of unusual inputs is necessary for security guarantees.
This was especially evident at London's recent CogX event, where industry thought leaders from around the world gathered to discuss the irrefutable impact that AI is already having across a multitude of sectors, and also to try and begin to answer some of these pressing questions. There is no doubt that AI and other technological advancements will change the way we live and work, and it will undoubtedly make some roles redundant. There are a multitude of positive examples demonstrating that AI is already impacting people's lives in positive ways. AI technology has huge potential to make people's lives better.
Microsoft now employs over 7,000 Artificial Intelligence (AI) research scientists and development engineers around the world under Microsoft Research (MSR) Executive VP Harry Shum, who shared the company's vision and strategy during the keynote address. Throughout this event, the company's executives focused "fostering efforts that lie at the intersection of AI, people and society", taking a feel-good approach that may lessen customers' fears of a Hal9000 AI nightmare future, positioning the company as a trusted advisor and provider of practical AI tools, products and services. As an example of the influence and impact of the massive Microsoft Research organization, the company announced a new initiative called AI for Earth, a program aimed at empowering people and organizations to solve global environmental challenges by improving access to AI tools, education and skills to accelerate innovation. This is yet another example where Microsoft has made it possible for enterprises to use, or experiment with, trained neural networks for AI tooling, potentially easing adoption of Microsoft AI technologies in risk-averse, and resource challenged, enterprise IT organizations.
A new competition heralds what is likely to become the future of cybersecurity and cyberwarfare, with offensive and defensive AI algorithms doing battle. "It's a brilliant idea to catalyze research into both fooling deep neural networks and designing deep neural networks that cannot be fooled," says Jeff Clune, an assistant professor at the University of Wyoming who studies the limits of machine learning. Machine learning, and deep learning in particular, is rapidly becoming an indispensable tool in many industries. "Adversarial machine learning is more difficult to study than conventional machine learning--it's hard to tell if your attack is strong or if your defense is actually weak," says Ian Goodfellow, a researcher at Google Brain, a division of Google dedicated to researching and applying machine learning, who organized the contest.
Artificial Intelligence enables marketers to personalise and create more effective customer experiences, and improve ROI. The second, the Zeitgeist Tool, again using Visual Recognition APIs, analysed trends of colours and styles from Instagram images, to help predict colors, styles, necklines, cuts and fabrics. Building APIs to measure social sentiment analysis is possible using natural language and tone analysis to understand what content is relevant to your brand, what the sentiment is and helps you to make decisions on which posts to act on. How: Our wonderful partner Servian has created a compelling solution which, leverages tone analysis, social sentiment analysis – and more – to help your organisation profile the persona of the best possible fit for your organisation, and then find that person – leveraging their CV, social profiles, references and more.
Candela -- bald, compact, thoughtful -- runs Facebook's Applied Machine Learning (AML) group, the engine room of AI at Facebook, which increasingly makes it the engine room of Facebook in general. So another aspect of machine learning is to approximate a large stored table with a function instead of going through every image. Candela describes the machine learning code he inherited as "robust but not the latest." H1, approaching product delivery, is where the product teams live -- the feeds team, the Instagram team, the ads team.
MIT's initiative that brings together problem-solvers of all stripes to tackle the world's pressing problems -- has four new global challenges for 2017: brain health; sustainable urban communities; women and technology; and youth, skills, and the workforce of the future. And, it builds and convenes a community of leaders who have the resources, the expertise, the mentorship, and the know-how to get each solution piloted, scaled, and implemented. "In the two and a half years since we first announced Solve, it has evolved in important ways. The May event celebrated the first cycle of Solvers, who worked on those 2016 challenges, by bringing them together with the Solve community to form partnerships to help implement their solutions.
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.