Machine learning focuses on the development of computer programs that can teach themselves to grow and adapt when exposed to new data. It is increasingly impacting our lives nowadays, as machines play an important role in banking and financial services, healthcare, retail, publishing, and in the social media, robot locomotion and gaming domains. When we start the journey of life as new born babies, we inherit the characteristics of our parents. We don't know what to do and when to do what. As we grow up, our parents and elders teach us how to walk, talk and take various decisions in our lives and, as time passes, we gain experience and knowledge.

Nominet today announces turing 1.3, the latest iteration of its DNS visualisation and analysis tool. The update adds a wide range of capabilities and enhancements to the platform, foremost of which is a new machine learning algorithm that allows turing to learn and understand the network analytics pattern of the user's business. The new functionality will be of huge benefit to cyber security experts and CIOs as it acknowledges the reality that enterprise networks aren't neat and structured environments where a simple security policy is enough to deliver protection, but a dynamic place where patterns and threats emerge and evolve on an ongoing basis. With these features, turing is the only product offering near real-time DNS traffic filtering and analysis down to the packet level, at enterprise performance and scale. Machine learning in turing makes use of third-party security feeds, which allows the tool to cross reference IP addresses and query names with known bad actors, and then provide the user with an overview of the top 10 events in their DNS traffic they need to know about at any one time.

Chatbots are here to stay. While a year ago, many in the tech industry saw this as yet another Silicon Valley fad that was more hype than substance, that debate has largely been put to rest now. Today bots are being deployed by major companies in almost every sector. But now, there is an intra-bot war brewing in the bot community. In October, we conducted a webinar on the fall of mobile apps and the rise of bots which had 145 attendees.

Presidents are powerful people facing and dealing with complex nation, market and economy sized problems. Making good decisions and managing these deeply complex systems and organizations requires massive amounts of data, analysis, experience and understanding. They must process and analyze millions of data inputs, and have a clear understanding of what the data in aggregate means, and then have an understanding of how they can influence the outcomes by pulling the right levers of power and influence to achieve their goals. The responsibility of dealing with these massive levels of complexity seems almost inhuman. In fact, so inhumane, perhaps it would be better to not have a human, but a bot as President.

As artificial intelligence begins to touch everything humans do, SoundHound Inc. is prepared to go up against tech giants such as Amazon Inc. (AMZN), Alphabet Corp.'s Google (GOOGL) and others to carve out its own niche. SoundHound, best known for its music recognition app, recently raised $75 million from backers like Nvidia Inc., Samsung Inc. and others to have device makers use tools that are built by SoundHound, rather then building the tools themselves. News of the funding was reported by Bloomberg and others. The $75 million funding round--which would be SoundHound's fifth according to Crunchbase--would value the company at roughly $800 million according to Pitchbook. SoundHound is trying to position its technology as being a gateway for device makers to add voice-enabled technology, allowing users to speak to their devices, similar to what Amazon has done with its Echo line of speakers and Google has done with its Google Home smart speaker.

Almost every day, there's news about a massive data leak -- a breach at Yahoo that reveals millions of user accounts, a compromise involving Gmail phishing scams. Security professionals are constantly moving the chess pieces around, but it can be a losing battle. Yet, there is one ally that has emerged in recent years. Artificial intelligence can stay vigilant at all times, looking for patterns in behavior and alerting you to a new threat. While AI is not anywhere close to being perfect, experts tell CSO that machine learning, adaptive intelligence, and massive data models that can spot hacking much faster than any human are here to help.

She asks about you, affirms things you have said, however finding out about her feelings is an elusive task. She is one of the earliest chatbots created by computer scientist Joseph Weizenbaum at MIT. Weizenbaum considered ELIZA to demonstrate an example of communication between machine and human, particularly highlighting the lack of depth in this exchange. Surprisingly, even with her short and repetitive utterances, some people attributed to her a level of human understanding. It wasn't that she could read the user's mind, rather, she affirms what the user says through a cascade of regular expression subsitutions used to tweak the user's input. An example is seen below, where ELIZA has picked up on the user's sentence structure and used substitution for her output as seen below.

IBM Watson is one of the most fascinating projects I'm following closely. Originally designed for a quiz game Jeopardy!, Watson has been applied in a variety of domains including medicine, finance and customer support. Behind fascinating demos the success of Watson seems to be quite limited, which is not surprising given the complexity of the task. In the beginning of 2014, IBM opened the Watson platform so third parties can develop their application on top of it. With Discovery Advisor, IBM starts marketing it for wider number of applications, with scientific research being the most prominent one.

Friday, 3rd of February, 2017, Baltimore, MD - Insilico Medicine today announced that it signed a Memorandum of Understanding (MOU) and started the first collaborative research project with one of the largest research and medical networks, Gachon University and Gil Medical Center. The intent of the long-term collaboration is to develop artificially intelligent multimodal biomarkers of aging and health status as well as interventions intended to slow down or even reverse the processes leading to the age-related loss of function. "We are happy to collaborate with Insilico Medicine, one of the leaders in AI with a specific focus on practical aging research in the pharmaceutical and healthcare industries. The field of artificial intelligence is rapidly evolving and in addition to our own cutting-edge research programs, we collaborate with other leaders to expedite progress and ensure that we can save and extend human life sooner", said Dr. Lee Uhn, Director of AI-based Precision Medicine at Gachon University, Gil Medical Center. The first MOU between the companies was signed on November 18th, but the first project launched and data exchange transpired in January 2017.

Babies learn about their world by pushing and poking objects, putting them in their mouths and throwing them. Carnegie Mellon University scientists are taking a similar approach to teach robots how to recognize and grasp objects around them. Manipulation remains a major challenge for robots and has become a bottleneck for many applications. But researchers at CMU's Robotics Institute have shown that by allowing robots to spend hundreds of hours poking, grabbing and otherwise physically interacting with a variety of objects, those robots can teach themselves how to pick up objects. In their latest findings, presented last fall at the European Conference on Computer Vision, they showed that robots gained a deeper visual understanding of objects when they were able to manipulate them.