General Electric’s healthcare division will partner with the corporate parent of two of Harvard University’s teaching hospitals to develop artificial intelligence products for medicine. The goal: to leverage the company’s dominant position in medical imaging into a new ownership of medical AI.
“We make machines but that’s not really the business,” says John Flannery, the chief executive of GE Healthcare, says he tells his team. “The business is what kind of solution can we put together that gets a better clinical and economic outcome?”
GE Healthcare is one of the main manufacturers of imaging devices used in radiology–things like PET and MRI scanners. The company also sells the computer software that doctors use to manage the images. This area, long thought to be low-hanging fruit for artificial intelligence applications, could form the basis of the first products to emerge from the collaboration. For instance, software might be able to identify which scans are normal, freeing human radiologists to focus on cases where there is an abnormality. (In 2015, IBM bought a company focused on reading radiology images with similar aims.)
The deal is between Partners Healthcare, the corporate parent of the Massachusetts General Hospital and the Brigham & Women’s Hospital, two Harvard-associated teaching hospitals.
“We’ve committed within our organizations to have a center that’s going to endure and the relationship with GE is intentionally 10 years,” Dr. Keith Dreyer, Chief Data Science Officer at MGH and BWH. “This is going to take a while to do. People ask me how long this is going to take to happen. And I say how long is it going to take for the Internet to happen?”
Dreyer says that the field of using AI in medicine has gone from hundreds of scientific publications to thousands over the past three years. He contrasts the effort to create an AI that would serve, essentially, as a virtual physician to a more realistic approach that is piecemeal, dealing with one opportunity at a time.
Flannery, the GE Healthcare CEO, is eager to move fast. He says commercialization of cloud-based radiology applications could occur in one to three years. “There will be an iTunes-like app store of programs offered to the physicians,” Flannery says. Other companies would then use the infrastructure and data created by GE and its partners to create their own programs, all of which will run on a single application. “If there were 20 technologies with these solutions, I can’t buy 20 solutions. I need a single platform,” Dreyer says. This remains an interesting space to watch.

Full article: https://www.forbes.com/sites/matthewherper/2017/05/17/ge-partners-with-harvard-hospitals-to-put-artificial-intelligence-in-medicine/#30e8838b7385

Contact Information:
https://www.forbes.com/sites/matthewherper/2017/05/17/ge-partners-with-harvard-hospitals-to-put-artificial-intelligence-in-medicine/#30e8838b7385

I thought this article published by IBM Watson deserved a post as it is a great example of artificial intelligence at work, in this case, the efficiency being created is so large I am sure others reading this will start to envision similar synergies for their companies and industries.

Overview

Korean Air has years worth of historical maintenance records for hundreds of aircrafts in its fleet. But until recently, this vast amount of critical data was virtually unsearchable. That meant that maintenance technicians had to diagnose and fix issues without being able to tap into or interpret implications from valuable past learnings and courses of action.

Enter Watson

Watson ingested structured and unstructured data from multiple sources including technical guidelines, non-routine logs, technician notes, inventory, trouble shooting time and material cost data, and in-flight incident history.

Watson Explorer, Natural Language Understanding and advanced content analytics locate previously hidden connections that helping maintenance crews diagnose and solve problems more quickly, with more confidence. Instead of spending hours diagnosing each potential issue, technicians can easily search and get near real-time analysis.

Further, if an issue occurs in flight, the cabin crew can report it immediately to ground operations. Watson will access data from similar issues in the past and compare this information against technical guidelines including necessary materials and fixing time. Maintenance technicians fix the issue on the ground and enter their actions into the system to add to Watson’s knowledge.

With Watson, maintenance managers can also identify trends of issues in each season and can take these insights to the original equipment manufacturers for improvement.

Over 200,000 maintenance cases per year are addressed 90% faster

Korean Air needs their over 2,000 maintenance employees to be able to act faster. When Watson delivered actionable insights on the root causes and solutions of issues, Korean Air shortened its maintenance defect history analysis lead times by 90%.

The maintenance employee can now see patterns of defect and failure on equipment to make preventive maintenance allowing them to spend more time getting people places on time—and working to keep their 25 million passengers happy.

Airlines, hospitals, businesses, educators and governments are working with Watson. In 45 countries and 20 industries, Watson is helping people make sense of data so they can make better decisions while uncovering new ideas.

How Korean Air worked with Watson

(In 5 simple steps)

1. Watson ingested a variety of structured and unstructured data related to maintenance for the hundreds of planes in Korean Air’s fleet.

2. Maintenance defect issues are reported by flight or cabin crews to ground operations.

3. Maintenance employees access data from look-a-like cases against technical guidelines.

4. Watson assists to decide probable cause and recommends solutions so that they can be quickly addressed by technicians.

5. More flights are on time, keeping 25 million passengers happy.

The full article can be seen here: https://www.ibm.com/watson/stories/airlines-with-watson.html

Its no secret I am following Artificial Intelligence and Tech developments that are rapidly moving towards disruption of industries such as legal, financial, health, retail, transportation and much more. For law firms and others in the legal industry the challenge now is to avoid becoming Sears, Kodak, OAG and others in long lists of other companies that once led industry segments only to miss technology evolutions and become obsolete. This recent press release form LexisNexis is an interesting example of legal tech at work.

MENLO PARK, Calif., March 31, 2017 /PRNewswire/ — LexisNexis today announced the first five participants in its new Silicon Valley legal tech accelerator program, which was created to give startups a leg up in the rapidly expanding legal tech industry. In line with LexisNexis’ broader vision to transform the way law is practiced, each of the accelerator participants is uniquely innovating in distinct areas of the law. After a thorough evaluation process, the five finalists – Visabot, TagDox, Separate.us, Ping, and JuriLytics – were selected from a list of 40+ promising startups for the interesting nature of their businesses and their innovative use of technology.

Based in the Menlo Park, CA offices of Lex Machina™, the program will leverage the vast content resources, deep expertise in legal, technology, and startup domains, and industry-leading market positions of LexisNexis and Lex Machina to guide and mentor program participants. The program will be led by Lex Machina CEO Josh Becker with support from LexisNexis’ Chief Technology Officer, Jeff Reihl, Chief Product Officer, Jamie Buckley, Vice President of US Product Management, Jeff Pfeifer, and Lex Machina Chief Evangelist, Owen Byrd.

The five charter members of the LexisNexis legal tech accelerator program are:

Visabot: An “immigration robot” powered by artificial intelligence that helps customers complete U.S. visa applications, including locating relevant open data about an applicant, guiding applicants in the process of gathering supporting documents, ensuring forms are filled out accurately, and drafting appropriate language to tell the applicant’s story.
TagDox: A legal document analysis tool that creates tags, allowing users to identify and structure information in a variety of document types, improving both the speed and the quality of the document review process; “tag results” can transform documents into easily readable summaries, checklists, database feeds or approval overviews.
Separate.us: A web-based application that automates legal document preparation for divorces and provides access to relevant professionals at affordable fixed rates, deploying a business model that targets both B2B and B2C customers.
Ping: An automated timekeeping application that collects all of a lawyer’s billable hours, capturing missed time and money (an estimated 20% across the industry), and operating entirely in the background in concert with standard legal billing software.
JuriLytics: An expert witness peer review service that attorneys can use to challenge their opponent’s experts with previously unobtainable credibility and bullet-proof their own expert’s work through vetting from the world’s top researchers (in any field of expertise).
Throughout the rigorous, 12-week curriculum, tech accelerator participants will gain knowledge and expertise in a variety of topics including technology and product development; running an agile product development organization; building a strong company culture; selling to legal departments and law firms; leveraging legal data; and best practices in customer success, marketing and fundraising. In addition, they will have access to a vast collection of enriched legal data and cutting-edge tools and technologies from LexisNexis, and will be able to leverage the company’s established relationships with Stanford University and other leading Bay Area schools, businesses, VCs and influencers to grow their companies.

“The LexisNexis legal tech accelerator is a promising initiative,” said Miriam Rivera, Managing Partner at Ulu Ventures and an advisor at the Venture Capital Director’s College, a part of The Rock Center for Corporate Governance at Stanford University. “As a legal tech investor and former Deputy GC of Google responsible for expanding the use of legal technology throughout the department, I am convinced the LexisNexis tech accelerator will not only foster innovation but also encourage new companies to thrive with sound business practices.”

For more information, or to apply to the tech accelerator program, please email Alex Oh (aoh@lexmachina.com).

About LexisNexis® Legal & Professional

LexisNexis Legal & Professional is a leading global provider of content and technology solutions that enable professionals in legal, corporate, tax, government, academic and non-profit organizations to make informed decisions and achieve better business outcomes. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services. Today, LexisNexis Legal & Professional harnesses leading edge technology and world class content to help professionals work in faster, easier and more effective ways. Through close collaboration with its customers, the company ensures organizations can leverage its solutions to reduce risk, improve productivity, increase profitability and grow their business. LexisNexis Legal & Professional, which serves customers in more than 175 countries with 10,000 employees worldwide, is part of RELX Group plc, a world leading global provider of information and analytics solutions for professional and business customers across industries.

About Lex Machina

Lex Machina’s award-winning Legal Analytics® platform is a new category of legal technology that fundamentally changes how companies and law firms compete in the business and practice of law. Delivered as Software-as a-Service, Lex Machina provides strategic insights on judges, lawyers, parties, and more, mined from millions of pages of legal information. This allows law firms and companies to predict the behaviors and outcomes that different legal strategies will produce, enabling them to win cases and close business.

Lex Machina was named “Best Legal Analytics” by readers of The Recorder in 2014, 2015 and 2016, and received the “Best New Product of the Year” award in 2015 from the American Association of Law Libraries.

Based in Silicon Valley, Lex Machina is part of LexisNexis, a leading information provider and a pioneer in delivering trusted legal content and insights through innovative research and productivity solutions, supporting the needs of legal professionals at every step of their workflow. By harnessing the power of Big Data, LexisNexis provides legal professionals with essential information and insights derived from an unmatched collection of legal and news content—fueling productivity, confidence, and better outcomes. For more information, please visit www.lexmachina.com.

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/lexisnexis-announces-first-five-legal-tech-accelerator-participants-300432478.html

SOURCE Lex Machina

Related Links

http://www.lexmachina.com

In my last post, I wrote about the handful of household brand name companies actually involved in Artificial Intelligence development, the usual suspects such as IBM, Microsoft, Intel, Apple and more – in a very quick guesstimate I threw out the number of tech professionals at work on AI at 300,000. I received some comments in that I was way short on my estimate and should take another look, so I did, but before I move on to a list of companies actively involved in AI one caveat, I am sure I will be short again.

Venture Scanner is tracking 957 Artificial Intelligence companies across 13 categories, with a combined funding amount of $4.8 Billion. Here are some of the names:

Baidu, Inc., Portland; Kasisito, Inc., New York City; Artivatic Data Labs, Bangalore; Ignite Tech, Vellore; Natxis, Paris; King, Stockholm; Fujitsu, Slovenia; Qikspace, Seattle; Lola, Boston; Pavlov, San Francisco; Cloud (Google), San Francisco; Next AI, Kitchner; Amazon, Seattle; Yik Yak, Atlanta; Operator, Inc., San Francisco; Chorus AI, San Francisco; AI Cities, San Francisco; AI.codes, San Francisco; Facebook, New York City; Intel, Portland; NVIDIA, San Francisco; Autodesk, San Francisco; Alexa, San Francisco; Degree Six, Chicago; Kount, Boise; UBER, San Francisco; X.AI, New York City; Microsoft, Seattle; OTSAW Digital, San Francisco; Cognitive AI, New York City; Google, San Francisco; All West/Select Sires, Portland; Vecna, Boston; Arkane Studios, Austin; Rockstar New England, Boston; Hex Entertainment, Orange County; Treyarch, Los Angeles; Knexus Research, Allentown; ABS Global, Madison; GGD Groningen, Groningen; Open AI (Tesla), San Francisco; AI + Club, San Francisco; Apple, San Francisco; Drive AI, San Francisco; LinkedIn, San Francisco; Bonsai AI, San Francisco; Voice Box Technologies, Seattle; Accenture, San Francisco; Knexus Research, Washington, DC; Yahoo!, San Francisco; Elemental Cognition, New York City; Mezi, San Francisco; Philips Research, Boston; MITRE, Albany; PWC Analytics, Boston; Artificial Brilliance, Los Ángeles; Zvelo, Inc., Miami/Ft. Lauderdale; and GE Global Research, San Francisco.

Venture Scanner organizes Artificial Intelligence into the 13 categories listed below, the full article can be seen here:

Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data.

Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads.

Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable representations. Examples include automated narrative generation and mining text into data.

Natural Language Processing (Speech Recognition): Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (General): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Personal Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Recommendation Engines and Collaborative Filtering: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

Full article can be seen here: https://venturescannerinsights.wordpress.com/tag/artificial-intelligence-company-list/