While the company, as usual, has been secretive about its upcoming products, recent developments like Apple's recent purchases of eye-tracking company SMI have lent credence to the notion that we might see the Apple smart glasses in the future. The device has started creating a buzz because recent supply chain leaks indicate it is being actively being worked upon. Integrating SMI's technology might make the Apple device consumer-oriented. Apple updated its Siri patent in China and the European Union earlier this year, mentioning that Siri would support smart glass capability.
At the TrailheaDX developer conference in San Francisco Wednesday, Salesforce announced three new tools that will make it easier for developers to incorporate artificial intelligence into custom apps. It's great news for companies looking to increase the efficiency of their customer service and inventory management. Another tool, Einstein Intent, gives developers the ability to sort customer inquiries by intent and then send relevant responses or personalized marketing. The third new tool announced Wednesday, Einstein Object Detection lets developers train models to recognize multiple unique objects within a single image.
The new tools, which harness Salesforce's in-house AI, Einstein, will give developers the ability to add features like natural language processing and image recognition to their apps for the CRM service. There are three new services available: Einstein Sentiment, Einstein Intent, and Einstein Object Detection. Sentiment gives developers the ability to create apps to sort messages by the perceived tone of the text as positive, negative, or neutral by identifying keywords, then automatically take appropriate actions. Intent provides a similar service, allowing developers to design apps to automatically classify what a customer is looking to accomplish by sending a message.
XGBoost is a well-loved library for a popular class of machine learning algorithms, gradient boosted trees. Given a Dask cluster of one central scheduler and several distributed workers it starts up an XGBoost scheduler in the same process running the Dask scheduler and starts up an XGBoost worker within each of the Dask workers. Dask.arrays use Numpy arrays, Dask.dataframes use Pandas, and now the answer to gradient boosted trees with Dask is just to make it really really easy to use distributed XGBoost. Bio: Matthew Rocklin is an open source software developer focusing on efficient computation and parallel computing, primarily within the Python ecosystem.
It sounds like Amazon Web Services is getting ready to bring translation technology used on the Amazon.com CNBC reported Monday that AWS will likely announce the availability of a machine translation service before the big re:Invent user conference this November. AWS has been first among cloud rivals many times in the past when it comes to releasing new services for its customers, one of the many reasons why it enjoys a leading portion of the market for cloud services. But it's playing catch-up here: Google has been working on computer-assisted translation for almost a decade as part of its search technology, and offers a translation API through Google Cloud Platform for its customers. After building out a huge number of services for basic developer needs, like compute and storage, cloud companies are increasingly jockeying for position based on higher-level services like artificial intelligence, which requires a substantial amount of investment that most companies are not prepared to make.
On the horizontal axis, the "marketing" function refers to a bot's ability to drive exposure, reach, and interaction with the brand or product for potential and current customers. The "support" function refers to a bot's ability to assist current customers with problems and to resolve those problems for them. Marketing bots tend to be largely campaign-driven, where they can be used effectively for driving engagement in short bursts. These are supporting tools used by the providers and brands or by bot developers.
SAN FRANCISCO -- Apple wants to make AR a reality. Three weeks after it unveiled ARKit, a new augmented reality developer kit that would help Apple app developers integrate this technology that overlays digital images on the physical world, and Apple CEO Tim Cook singed its praises, the company has acquired SensoMotoric Instruments, a German developer of eye-tracking movement technology. "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans," the company said in an email statement. Longtime Apple analyst Tim Bajarin, president of Creative Strategies, believes the company could leverage AR for use in the home improvement and real estate industries.
However, let's remember that it was on the forefront of deep learning with products like the Aibo robot dog, and has used it recently in the Echo-like Xperia Agent (above) and Xperia Ear. Sony joins its rivals Google, Facebook, Microsoft, Apple, Amazon and others in making its AI open source. On one hand, it will help developers build smarts into products, and on the other, Sony is hoping that developers will "further build on the core libraries' programs," it writes. However, Sony's AI offerings are certainly unique.
New Delhi: Tech giant IBM is looking at engaging with developers in India in areas like artificial intelligence, machine learning and Internet of Things (IoT) to help them hone their skills for new technology trends. From India's perspective, we think it is the right time for developers to work on technology areas like AI, ML, data science and we want to help them in their journey," IBM India/SA country leader developer ecosystem and start-ups Seema Kumar told PTI. Using IBM technology, say for example Watson, developers can come out with solutions that can help solve problems in not just their community but also in others, she said. IBM's Watson is a cognitive artificial intelligence platform that can analyse huge amounts of data.
Propelled by massively parallel computer systems, huge datasets, and better algorithms, AI has brought a number of important applications, such as image- and speech-recognition and autonomous vehicle navigation, to near-human levels of performance. In reinforcement learning, systems are not trained in advance with huge amounts of labeled data--which is called "supervised learning"--but are simply rewarded when they get the right answer. According to Yann LeCun, director of AI research at Facebook, supervised learning is the dominant AI method today, while reinforcement learning occupies a niche mostly in games. Another promising new approach to unsupervised predictive learning lies in something called generative adversarial networks (GAN), in which two neural nets train themselves by competing in a zero-sum game to produce photorealistic images.