No industry is as driven by the technology transformation as fintech is. This is both good and bad news. On the one hand, fintech is on the front line of exploring advanced innovations while other industries still wait in line. However, with so many companies implementing digital technologies simultaneously, it's challenging to keep track of recent tendencies and stand out from the competition. The only winning strategy here is analyzing the tendencies and disruptive technologies and starting to implement them before the competitors brought about the change in your field.

The robotic process automation (RPA) market has heated up as enterprises increasingly look to RPA bots to achieve their long-term automation ambitions. Meanwhile the RPA tools market has been rapidly evolving as new vendors enter the market, mature vendors add new features, and RPA use cases expand beyond traditional applications in response to the platforms adding cognitive and AI capabilities. However welcome these new capabilities may be, they muddy the waters for enterprises that are evaluating RPA tools. TechBeacon talked to top experts and analysts to provide some clarity. Here are the variables to consider as you shop for the right RPA tools for your organization.

TEL AVIV (Reuters) - Walmart said on Tuesday it has acquired Aspectiva, an Israeli start-up whose technology analyses consumers' product reviews to help shoppers make decisions. Financial details were not disclosed. Aspectiva will join Walmart's Store No 8, the incubation arm launched by the U.S. retailer in 2017 to find new commerce-related technologies. Aspectiva has developed machine-learning techniques and natural language processing capabilities, "areas we believe will have profound impact on how customers will shop in the future," Store No 8 principal Lori Flees said. Walmart also has a strategic investment in Team8, an Israeli cybersecurity start-up incubator, and launched a joint venture with Eko, an interactive media and technology company with offices in Tel Aviv and New York.

Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3-D printing or lithography. Since the connection between the input and output planes of a diffractive neural network is established via diffraction of light through passive layers, the inference process and the associated optical computation does not consume any power except the light used to illuminate the object of interest. Developed by researchers at UCLA, diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform that can find numerous applications in robotics, autonomous vehicles, defense industry, among many others. In addition to providing statistical inference and generalization to classes of data, diffractive neural networks have also been used to design deterministic optical systems such as a thin imaging system.

Put simply, conversational AI refers to technology that can automate communication by using speech-based assistants, messaging apps and chatbots. This helps businesses in creating a highly personalized experience at scale. Today, conversational AIs are more popular than ever, and their rapid advancement points to a future where we are likely to become more reliant on this technology for our daily tasks. The best UI UX design services are requested time and again by businesses to integrate conversational AIs into their operations. For example, you or someone you know may have Google Home or Amazon Alexa at home.

In the next video we will look at different data sources and how to clean the data.

How do security professionals view artificial intelligence for helping protect their enterprises? A new SANS survey will examine perceptions about the basic capabilities of AI for security and what technologies – including deep learning, various recognition techniques, machine learning, and others – are considered part of AI for security. The survey also examines whether, how, and when security experts will begin implementing AI for security and how they intend to use it.

Here at VTEX Voice Solutions our primary focus is on document creation using speech recognition. There has been a lot off buzz about AI and machine learning in all industries and it applies to what we do as well. Documentation in the medical field is a critical component and the all major medical Speech Recognition products like Dragon Medical Practice Edition from Nuance, SayIt from nVoq and the Fluency Direct from 3M use some sort of machine learning or AI to help improve accuracy. Accuracy is critical in medical documentation and advances in AI and Machine learning are helping to make documentation errors a thing of the past. We may not be at the point where you overhear your surgeon saying, "Hey, Google, pass the scalpel," but artificial intelligence (AI) is gradually making its way into the healthcare industry and, by extension, dermatology and plastic surgery practices, too.

The Artilects... are you a terran, a cyborg or a cosmist? Take the poll in the video.

The hype machine for AI and machine learning has been going full throttle, and one can be forgiven for thinking that every organization from the mega-techs to the corner store is turning over processes or decisions to AI. If you're still stuck trying to figure out how AI and machine learning can fit into your operations, don't worry -- so is everyone else, actually. Companies may be increasing their investments in machine learning and machine learning development, but, for the most part, are still in the early learning stages. That's the major takeaway from a survey of 750 technology managers and professionals released by Algorithmia, which specializes in such things. Survey respondents represent companies that are actively engaged in building machine learning lifecycles.