Making human doctors obsolete

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You may think that artificial intelligence (AI) will make doctors obsolete soon but that day is still far off. In fact, computers are not that intelligent just yet. Most computer solutions emerging in healthcare rely on algorithms written to analyse data and recommend treatments. They do not rely on computers thinking independently. The computers in question are fed with large amounts of known data and use rules or algorithms set by experts to extract information and apply it to a health issue or problem.


Automatic Image Captioning with CNN & RNN

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So, this is the second Computer Vision project that I have implemented. If you haven't checked out the first project that is the Facial Keypoint Detection's blog already, I'll leave a link here. Now, You might think what in the world is image captioning? and How can it be done automatically? Okay! so, in order to explain that to you in simple "gestures", let me introduce, the almighty Pikotaro. Generally, a captioning model is a combination of two separate architecture that is CNN (Convolutional Neural Networks)& RNN (Recurrent Neural Networks) and in this case LSTM (Long Short Term Memory), which is a special kind of RNN that includes a memory cell, in order to maintain the information for a longer period of time.


A Realistic Framework for AI in the Enterprise - InformationWeek

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The year 2019 may have been a bit of a disappointment for enterprises in terms of their efforts to deploy artificial intelligence at scale. While executives in the C-suite remain committed to the idea of AI, and many organizations have created successful pilots of some form of AI technology, getting from pilot to scale has proven a greater challenge. How do you get from here to there? A new report from Lux Research offers a view into the state of AI in the enterprise today, perspective on its history, and some practical advice on a programmatic framework for how to think about AI in the enterprise and tackle implementation challenges. "Given the massive amounts of hype and promise surrounding AI and related technologies like machine learning and deep learning, it's become increasingly difficult to make critical innovation and investment decisions in the space," writes Lux analyst and lead report author Cole McCollum.


Listen to this AI voice clone of Bill Gates created by Facebook's engineers

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We're headed for a revolution in computer-generated speech, and a voice clone of Microsoft founder Bill Gates demonstrates exactly why. In the clips embedded below, you can listen to what seems to be Gates reeling off a series of innocuous phrases. "A cramp is no small danger on a swim," he cautions. "Write a fond note to the friend you cherish," he advises. But each voice clip has been generated by a machine learning system named MelNet, designed and created by engineers at Facebook.


Google Cloud says HIMSS20 attendees should eye AI, interoperability and security

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There will be many technologies, strategies and trends discussed at HIMSS20 in March. But which are most important? Which deserve the time and attention of healthcare CIOs and other leaders? Healthcare IT News spoke with Dr. Joe Corkery, director of product, healthcare and life sciences, at Google Cloud (Booth 3729), to get his expert view of the health IT terrain. He identified what he says are three very important trends and technologies for HIMSS20: AI in healthcare, data interoperability, and data security and privacy.


Not Bot, Not Beast: Scientists Create First Ever Living, Programmable Organism

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A remarkable combination of artificial intelligence (AI) and biology has produced the world's first "living robots." This week, a research team of roboticists and scientists published their recipe for making a new lifeform called xenobots from stem cells. The term "xeno" comes from the frog cells (Xenopus laevis) used to make them. One of the researchers described the creation as "neither a traditional robot nor a known species of animal," but a "new class of artifact: a living, programmable organism." Xenobots are less than 1 millimeter long and made of 500-1,000 living cells.


Adventures With Artificial Intelligence and Machine Learning

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Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a "data scientist in a box". I'll start by saying that machine learning and'artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways. The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a naïve Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didn't have real data and only had a conceptual problem to be solved.


Adventures With Artificial Intelligence and Machine Learning

#artificialintelligence

Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a "data scientist in a box". I'll start by saying that machine learning and'artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways. The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a naïve Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didn't have real data and only had a conceptual problem to be solved.


OntologySummit - OntologPSMW

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An annual series of events (first started by Ontolog and NIST in 2006) that involves the ontology community along with communities related to each year's theme chosen for the summit. Format: each year's Summit comprises of a series of both virtual and face-to-face events that span about 3 months. These include a vigorous three-month online discourse on the theme of choice (for a particular summit), virtual panel discussions, research activities ... etc. which will culminate in a two-day face-to-face workshop and symposium. The publication of a Summit Communiqué each year, at the end of the face-to-face symposium, to get an annual message from the participants to the world-at-large, has also been a signature activity of this Ontology Summit series. There is now an Ontology Summit YouTube Channel at Ontology Summit YouTube Channel.


Update on Federal Register Notice on Artificial Intelligence (AI) Patent Issues JD Supra

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In the decision, the UKIPO Hearing Officer, Huw Jones, citing sections 7 and 13 of the Act (The Patents Act 1977) and Rule 10 of the Rules (The Patents Rules 2007), Officer Jones said "the Office accepts that DABUS created the inventions" in the patent applications but that as it was a machine and not a natural person, it could not be regarded as an inventor. Moreover, as DABUS has no rights to the inventions, the Officer stated it is unclear how the applicant derived the rights to the inventions from DABUS: "There appears to be no law that allows for the transfer of ownership of the invention from the inventor to the owner in this case, as the inventor itself cannot hold property." Id. at p. 6. Officer Jones further noted that while he agreed inventors other than natural persons were not contemplated when the EPC was drafted, "it is settled law that an inventor cannot be a corporate body." Accordingly, since the "applicant acknowledges DABUS is an AI machine and not a human, so cannot be taken to be a'person' as required by the Act." However, the Hearing Officer also added that the case raised an important question: given that an AI machine cannot hold property rights, in what way can it be encouraged to disseminate information about an invention?