Facebook uses photos of marijuana and broccoli to show how its AI has gotten smarter

Daily Mail - Science & tech

Facebook's AI software is getting better at detecting inappropriate and dangerous content on its site. To demonstrate this, Facebook's chief technology officer, Mike Schroepfer, put a photo of marijuana and broccoli up next to each other on stage at the firm's F8 developer conference. He said the social media giant's'state of the art' computer vision is now capable of detecting the difference between food and drugs in a matter of seconds. The hope is that this kind of software can help Facebook clean up the site more quickly and with greater accuracy than humans ever could. Facebook's CTO Mike Schroepfer (pictured) put a photo of marijuana and broccoli up next to each other on stage at the firm's F8 developer conference to show how its AI is getting smarter Facebook's AI was 93.7 percent sure that it was looking at an image of marijuana, while it was 88.3 percent sure it was looking at an image of tempura broccoli.

Consumerism will force healthcare's hand on interoperability, Forrester finds at HIMSS19


Forrester Research has published a report summing up its impressions from the HIMSS19 Global Conference & Exhibition. Experts said they came away from the show convinced that big momentum is building behind interoperability, and it's not coming from the places one might expect. Health systems will need to do better with the management and sharing of more data than ever if they hope to stay competitive in a value-based care landscape where patients have more choice than ever about where they get their care, according to the study. WHY IT MATTERS As interoperability continues to gain steam, it's set to boost the profiles of an array of other key technologies, said Forrester researchers. At HIMSS19, it was clear that tools "supporting data management and interoperability, including cloud and AI, showcased their ability to add value and hit on the quadruple aim: improving the customer experience, driving better outcomes, lowering costs, and supporting the whole care team," they said.

Graph Data on the Web: extend the pivot, don't reinvent the wheel Artificial Intelligence

This article is a collective position paper from the Wimmics research team, expressing our vision of how Web graph data technologies should evolve in the future in order to ensure a high-level of interoperability between the many types of applications that produce and consume graph data. Wimmics stands for Web-Instrumented Man-Machine Interactions, Communities, and Semantics. We are a joint research team between INRIA Sophia Antipolis-M{\'e}diterran{\'e}e and I3S (CNRS and Universit{\'e} C{\^o}te d'Azur). Our challenge is to bridge formal semantics and social semantics on the web. Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. In this position paper, we emphasize the need to extend the semantic Web standard stack to address and fulfill new graph data needs, as well as the importance of remaining compatible with existing recommendations, in particular the RDF stack, to avoid the painful duplication of models, languages, frameworks, etc. The following sections group motivations for different directions of work and collect reasons for the creation of a working group on RDF 2.0 and other recommendations of the RDF family.

Setting Expectations Around AI in Healthcare


There's a lot of hype and promises to artificial intelligence (AI) in healthcare. But what can be done to reap the full benefits of this technology? READ: What Is Being Done About Healthcare's Lack of Interoperability? Michael Doyle, CEO of COTA, told Healthcare Analytics News that interoperability is a factor in how AI works. AI needs data to learn.

Challenges of Artificial Intelligence in Healthcare


Researchers and scientists consider artificial intelligence (AI) as similar to electricity; it will be a transformative tool in every industry, even in healthcare. Predictions for 2019 claim there will be an increase of AI transactions in healthcare. To support this, more than a third of hospitals and healthcare institutions plan to integrate AI into their system within the next two years. However, AI still faces a lot of challenges in the field of healthcare, especially when it comes to data protection and predictive solution, which are discussed below. Data are the primary source of learning of machines, including AI.

After interoperability: FHIR is the gateway for AI


HL7's FHIR (Fast Healthcare Interoperability Resources) is largely seen as an enabler of health data exchange, which of course it is, but executives at IBM, Google and Microsoft said it will also lay the foundation for artificial intelligence and machine learning. "Interoperability is the cornerstone of our healthcare strategy -- teaching cloud to speak the language of healthcare: HL7, FHIR, DICOM," said Aashima Gupta, global head of healthcare and life sciences at Google Cloud, during a panel discussion here at HIMSS19 on Thursday. Google, in addition to IBM, Microsoft, Oracle and Salesforce, signed a pledge to remove interoperability barriers back in August 2018 during the Blue Button 2.0 hackathon at the White House. And while the companies have yet to provide specific details they said it will involve cloud computing, FHIR and open APIs. "We're competitors in many ways but also very much aligned because without interoperability we can't really make a change and make a difference," said Mark Dudman, health of global product and AI development at IBM. "Right now, FHIR is taking systems that don't interact to talk quickly. We've hit that first real target of getting systems to talk, but now we have to talk in volume."

Announcing ML.NET 0.10 - Machine Learning for .NET


ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models. ML.NET allows you to create and use machine learning models targeting common tasks such as classification, regression, clustering, ranking, recommendations and anomaly detection. It also supports the broader open source ecosystem by proving integration with popular deep-learning frameworks like TensorFlow and interoperability through ONNX. Some common use cases of ML.NET are scenarios like Sentiment Analysis, Recommendations, Image Classification, Sales Forecast, etc. Please see our samples for more scenarios.

AI Powered EHR


Artificial intelligence is like the air around us, it is practically everywhere. From the palm of our hands, in the form of our smart watches and phones to our heads; MIT's Alter Ego project is based on a cognitive helmet which allows humans to interact with machines in Natural Language, through the process of brainstorming – without speaking a word. AI powered EHR has had many successes in the healthcare industry; based on a Chinese trial: An AI system programmed to aid brain scans, scored a higher score in accuracy as compared to its human counterparts. The patient which was predicted by the system to be revived from a coma, did indeed woke up. Whereas, the doctors who took part in this experiment had proposed totally different outcomes.

More than Meets the AI - Data Matters


Artificial Intelligence is all over the place. If you attended our post-summit AI symposium we held during Global Summit 2018 in San Antonio, you certainly got a taste of the varied use cases where AI can make a difference. But what does it take to build an AI-powered application? Do you start implementing tedious data-gathering processes for training your models? Or do you first scour the job market for a handful of those elusive data scientist unicorns, which itself may take years?

Data Governance, AI, and Data Driven Medicine: Challenges & Opportunities


On behalf of Dimensional Concepts LLC (DCL) of Reston, Virginia I attended a meeting sponsored by the DC-based think tank Center for Data Innovation titled U.S. Data Innovation Day 2018: The Future of Data-Driven Medicine. What data governance challenges are associated with applying AI techniques to new drug discovery and development? What data governance challenges are specific to medical AI applications? Which data governance challenges are generic? What are the implications of using AI techniques for the business processes and regulations associated with new drug development and medical treatment delivery?