BigML is a leading Machine Learning company that helps other businesses make highly automated, data-driven decisions. BigML was founded in 2011 in Corvallis, OR, USA and made its European expansion official by opening its European headquarters in Valencia, Spain in July 2015. The company has pioneered the Machine Learning as a Service (MLAAS) wave of innovation through its consumable, programmable, and scalable software platform streamlining the creation and deployment of smart applications powered by state-of-the-art predictive models. To date, BigML has 15 patent applications, more than 25,000 customers from 120 countries and 5M in venture capital investment. In addition to commercial activities, BigML is playing an active role in promoting Machine Learning in education around the world through its rapidly expanding ambassador program thanks to active participation from top universities in diverse geographies such as North America, Europe and Africa, making real BigML's motto: "Machine Learning made beautifully simple for everyone".

Machines could gradually come to replace doctors, usurping their technical and surgical know-how. However, this could be good news for the patient. Doctors would be more available to counsel and reassure their patients. In this way, machines deployed in the medical field could actually help to bring more of a human touch to the doctor-patient relationship. This is the prediction put forward by Guy Vallancien – a surgeon, member of the French National Academy of Medicine and President of the European School of Surgery – in his book'La médecine sans médecin.

Don Norman is without a doubt one of the most influential voices within the UX and design industry and also the cognitive sciences. Don produced some of the earliest literature about human-centered design within the digital space – he also coined the term "user experience" whilst working at Apple. In part 2 of our conversation with Don, we talk about whether technology is making us dumber or smarter, living with complexity, as well as AI, agents and their role in the future of healthcare.

The last few weeks, it's become nearly impossible to scour the latest in tech news without coming across something related to the idea of "bots." Bots, per their current implementations, are bits of software AI geared toward helping users complete tasks, request information or even shop online. Their usage isn't limited to these things and may also consist of programs used to carry out mundane or time consuming tasks that might have otherwise required a human. The idea around bots and their proposed usage is pretty straight-forward. As internet communications, cloud computing and complex machine learning improves, there are companies well positioned to leverage the power of bots to make all sorts of tasks faster and easier for users of modern technology.

Google CEO Sundar Pichai thinks we're hurtling towards a future where you'll rely on artificial intelligence more than your smartphone. "In the long run, we're evolving in computing from a'mobile-first' to an'AI-first' world," he said on Alphabet's Q1 earnings call. Google has long used a ton of AI and machine learning in its core products.

In my last post I promised to use some real-world use cases from the recent Verizon Data Breach Digest report to illustrate potential ways that machine learning be can used to detect or prevent similar incidents. For my first example, I've chosen the case of a manufacturer whose designs for an innovative new model of heavy construction equipment were stolen following a social engineering attack. They were tipped off when a primary competitor, located on another continent, introduced a new piece of equipment that looked like an exact copy of a model recently developed by the victim company. To paraphrase the Verizon report, it went like this. The threat actors identified an employee who they suspected would have access to new product design they were after -- the chief design engineer.

The future of cyber-security looks part human and part machine, according to MIT's Computer Science and Artificial Intelligence Laboratory but what does the broader industry think? According to researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), working with machine-learning startup, PatternEx, the future of cyber-security could be part-human and part-bot. A newly published paper from MIT suggests that the prototype AI2 system it has developed combines machine learning with human analysis to end up with an 85 per cent successful cyber-attack prediction rate. The MIT researchers maintain that analyst-driven security systems miss too many attacks as they rely upon humans to create rules that have to be matched. On the other hand, machine-learning solutions rely upon anomaly-detection which is prone to triggering false positives and so lead to mistrust.

The hype around artificial intelligence (AI) is ramping up, especially as big tech companies like Amazon, Google, Facebook, IBM and Microsoft attempt to commercialize its use. Agencies are also starting to figure out how they can leverage AI to make their clients' marketing and advertising efforts more effective. Josh Sutton: We've been looking at AI for the better part of four years now with the real focus of getting an understanding as to what it really is that people mean when they say "AI." Within the practice, we primarily do two things. One is that we help companies look across the landscape of all the different big data, machine learning and other artificial intelligence platforms that are available to understand which ones can help them best achieve their business objectives.

Algorithms and open-source machine-learning tools are as good as, or even better than, human reviewers in detecting cancer cases using information from free-text pathology reports, according to a new study from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University at Purdue. Further, the computerized approach also was faster and less resource-intensive. Researchers sampled 7,000 free-text pathology reports from more than 30 hospitals that participate in the Indiana Health Information Exchange. The researchers used open-source tools, classification algorithms, and varying feature selection approaches to predict if a report was positive or negative for cancer. The results indicated that a fully automated review yielded results similar or better than those of trained human reviewers, saving both time and money, Indiana University said.

Algorithms and open-source machine-learning tools are as good as, or even better than, human reviewers in detecting cancer cases using information from free-text pathology reports, according to a new study from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University at Purdue. Further, the computerized approach also was faster and less resource-intensive. Researchers sampled 7,000 free-text pathology reports from more than 30 hospitals that participate in the Indiana Health Information Exchange. The researchers used open-source tools, classification algorithms, and varying feature selection approaches to predict if a report was positive or negative for cancer. The results indicated that a fully automated review yielded results similar or better than those of trained human reviewers, saving both time and money, Indiana University said.