machine learning


AI In Medicine: Rise Of The Machines

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Could a robot do my job as a radiologist? If you asked me 10 years ago, I would have said, "No way!" But if you ask me today, my answer would be more hesitant, "Not yet -- but perhaps someday soon." In particular, new "deep learning" artificial intelligence (AI) algorithms are showing promise in performing medical work which until recently was thought only capable of being done by human physicians. For example, deep learning algorithms have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with astonishing accuracy.


Why Call It 'Machine Learning' When It Has Nothing To Do With Thinking Machines?

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Why call it machine learning when it has nothing to do with thinking machines? Machine learning may not have much to do with thinking machines today, but that wasn't always the case. It's important to realize that when the terms "Machine Learning" and "Artificial Intelligence" were coined in the late 50s, the goal was very much to create thinking machines. They not only thought what we now call "Artificial General Intelligence" was possible, but that it was bound to happen within a matter of decades if not years. And furthermore, they thought that the various things they were exploring -- like logical programming and machine learning -- would be enough to get them there.


Machine learning predicts World Cup winner

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The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.


Google Machine Learning Crash Course adds lesson on ensuring AI fairness

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Earlier this week, Google announced that it was piloting a machine learning intensive for college students. Today, its broader Machine Learning Crash Course is adding a new training module on fairness when building AI. As adoption of machine learning continues, ethics and fairness are very important considerations. While AI can have the "potential to be fairer and more inclusive at a broader scale than decision-making processes based on ad hoc rules or human judgments," there might be underlying biases present in the data used to train these models. Other issues involve insuring that AI is fair in all situations, while more broadly there is "no standard definition of fairness."


Trends in Big Data and Artificial Intelligence Data

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The major trend observed across industry and the public sector is artificial intelligence (AI)/machine learning (ML) for automation. This, in turn, plays a major part in any digital transformation journey. The trend grew out of the Bay Area, providing a customer-centric view of data and often involved using data as part of the product or service. This consumer- or customer-centric model assumes data enrichment with data from multiple sources. However, fundamentally, it divides the data into two main areas.


Why We Should Stop Conflating Human and Machine Intelligence

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It's common to hear phrases like'machine learning' and'artificial intelligence' and believe that somehow, someone has managed to replicate a human mind inside a computer. This, of course, is untrue--but part of the reason this idea is so pervasive is because the metaphor of human learning and intelligence has been quite useful in explaining machine learning and artificial intelligence. Indeed, some AI researchers maintain a close link with the neuroscience community, and inspiration runs in both directions. But the metaphor can be a hindrance to people trying to explain machine learning to those less familiar with it. One of the biggest risks of conflating human and machine intelligence is that we start to hand over too much agency to machines.


The AI revolution is underway – what does the future look like for banks?

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Artificial intelligence (AI) is seemingly everywhere these days, from Siri on an iPhone to calling an Uber or watching that video recommended on Netflix based on predictive algorithms. While AI hasn't quite taken over just yet, it's clear that it will become even more deeply embedded in our lives. The banking industry, as much as any other, has the potential to proactively harness AI technology to transform itself, or use it to just keep up or get left behind. With the market changing rapidly not only due to advanced digital technologies, but also emerging competition from fintechs and more knowledgeable, demanding customers, banks are faced with a number of challenges. To maintain market share and profitability in an industry where competing products and services are frequently very similar to their own, banks need to focus on what improves the customer experience to distinguish them from their peers and proactively service their customers.



How is IBM Research strengthening security in machine learning?

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Machine learning and AI systems need data to function, but they also need to be actively protected. IBM researcher Dr Irina Nicolae is applying her skills to these complex issues. Dr Irina Nicolae is a research scientist at IBM Research Ireland. With a background in computer science and software engineering and a PhD in machine learning (ML), Nicolae has carved out a career battling one of security's most pressing issues: protecting artificial intelligence (AI) and ML systems from attacks. The quality of the data and its relevance to the task are the most important points to consider.


Azure Machine Learning Deployment Workflow – Microsoft Azure – Medium

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This blog post is co-authored by Jaya Mathew and Francesca Lazzeri, data scientists at Microsoft. The Artificial Intelligence Conference in London is a relatively addition to the list of conferences hosted by O'Reilly worldwide. The aim of this conference is to create a forum for the ever-growing AI community to explore the most essential issues and innovations in applied AI. In the conference the various talks covered topics ranging from practical business applications of AI, to compelling AI enabled use cases, to various technical trainings and deep dive into successful AI projects etc. In our session "A day in the life of a data scientist in an AI company", we presented a scientific framework to help organizations to systematically discover opportunities to create value from data, qualify new opportunities and assess their fit and potential, then how to build a team to smoothly implement end-to-end advanced analytics pilots and projects, and produce sustainable ongoing business value from data.