Bots, done right, are the cutting-edge form of interactive communications that captivate and engage users. But what kind of potential do they have for sales, customer support and the bottom line? Tobias Goebel, Aspect Software's Director of Emerging Technologies, weighed in at our recent VB Live event. Recently, I had the opportunity to exchange thoughts and opinions on artificial intelligence, machine learning in general, and chatbots in particular with Ron Brachman from Cornell Tech and Akhil Aryan from Haptik. It was all part of a VB Live event hosted by VentureBeat.

Connected baggage, robots, virtual reality, big data, and drones will be part of your travels soon. Just how will they, along with other technology trends, create a better experience for travelers? Robotics combined with artificial intelligence You can already experiment with robots that deliver room service, guide passengers to departure gates, or provide translation assistance. But would they be able to understand informal language such as slang, idioms, local dialects or irony? Combining their capabilities with Artificial Intelligence (AI) so they are able to recognize emotions, group behaviors, and proactively respond to unexpected situations, give them the potential to provide the next generation of customer service.

Since the nineteenth century, the primary tool used to identify cells has always been the microscope but the report, by the Harvard team, identified many problems with this system. Using this technique they were able to make the AI accurate in 92 per cent of diagnosis and decrease the human rate of error by 85 per cent. Importantly, the errors made by the deep learning system did not generally correlate with the errors made by humans. The report concluded: "Although the pathologist alone is currently superior to our deep learning system alone, combining deep learning with the pathologist produced a major reduction in pathologist error rate."

Since the nineteenth century, the primary tool used to identify cells has always been the microscope but the report, by the Harvard team, identified many problems with this system. Using this technique they were able to make the AI accurate in 92 per cent of diagnosis and decrease the human rate of error by 85 per cent. Importantly, the errors made by the deep learning system did not generally correlate with the errors made by humans. The report concluded: "Although the pathologist alone is currently superior to our deep learning system alone, combining deep learning with the pathologist produced a major reduction in pathologist error rate."

Self driving cars which seemed like fiction in not so recent past, have become a hot topic in the auto and tech industry. New players like Google, Baidu, Tesla are jumping into this sector has heated up competition and as of current day, almost all car manufacturers have announced their development efforts towards autonomous driving. It is now broadly clear that vision sensing elements like cameras, LIDAR ( like Velodyne's LIDAR seen on top of Google's self driving cars), RADARs, SONARs, night vision systems, proximity sensors, etc. are the most important enabling elements for self driving technology. These sensors are mounted across the vehicle body to form real time 3D image of the surroundings. These images are then compared to already existing 3D maps and intelligent algorithms are able to take the appropriate action to drive the car without any manual intervention.

Apple's biggest move was to open up the talking iPhone assistant to third-party developers for inclusion in their apps, paving the way for users to hail a ride from Uber or send a message with Tencent's WeChat using voice commands. Experts in artificial intelligence applauded the move as an important step forward, in part because the more people use an artificial intelligence system, the better it becomes. But some wondered why Apple had not made Siri an open platform much sooner, noting that competing products including Amazon.com's Alexa, Microsoft's Cortana and the Google app are already open to developers. "Is it too little too late?" "Siri is five years old and still trying to learn how to play well with others."

The International Symposium on Biomedical Imaging set the challenge between October 2015 to April 2016 to encourage research into identifying breast cancer by computers rather than by pathologists. Since the nineteenth century, the primary tool used to identify cells has always been the microscope but the report, by the Harvard team, identified many problems with this system. These included a lack of standardization across the board, diagnosis errors and the time it takes for pathologists to manually load millions of slides each year. Utilisting deep learning, and feeding the machine hundreds of slides showing both cancerous and non-cancerous lymph nodes, scientists were able to train AI to pick out hazardous cells. Using this technique they were able to make the AI accurate in 92 per cent of diagnosis and decrease the human rate of error by 85 per cent.

Text Mining is a general catch-all for a range of techniques for extracting information from text strings. Being able to extract, clean and summarize text data is a key ability for any Data Scientist. The following blog aims to highlight some of the process steps I use to clean text data as well as some summarization methods. To illustrate some of the approaches to text mining I am going to use the full text of 1984 by George Orwell. This data was extracted from msxnet.org/orwell with analysis carried out in R.

Ability to drive technical development and prototyping in a fast-paced startup-like environment. Strong understanding and ability to apply advanced mathematical concepts to solve real world problems. The Machine Learning Data Scientist will come from a very strong background in mathematics, applied mathematics, statistics and computer science (at least MA/MSc level, Engineer school/PhD from university a plus). A post-graduate degree in Machine Learning, Artificial Intelligence or a related technical field is a strong plus (any ML background will be considered). Global Markets Labs is a spin-off quantitative research team which mandate is to build the next generation of Data Intelligence and language understanding products used in the Banks Global Markets. This small team works on projects using the latest techniques in Artificial Intelligence, Datamodelling and Natural Language Understanding.

Machine learning is infiltrating many industries. Marketers are using complex data algorithms to target customers based on their behaviours, while urban planning firms are creating better transport systems, and health organisations are detecting diseases earlier. Last year, Amazon professor of machine learning at the University of Washington, Carlos Guestrin, said that in the next five years, every successful breakthrough app will use these methods at its core. But in the highly complex field of cancer, it's a more laborious and challenging task, according to professor Mathukumalli Vidayasagar, a US-based control theorist who has been working with machine learning methods since the 1990s. Vidayasagar is a Fellow of the Royal Society at the University of Texas and keynote speaker at the University of Melbourne's'Thinking Machines in the Physical World' conference yesterday.