Any time of year is the perfect time to buy a robot for yourself or someone who needs more robots in their life, but this particular time of year is even perfecter than most: The holidays are approaching, all kinds of things are on sale, and nobody will ask questions if a whole bunch of new robots suddenly show up in your house. To help you decide which robots to buy for yourself and which to buy for yourself and for other people, we've put together a brand new edition of our annual Robots Gift Guide. It's stuffed with giftable robots ranging from affordable to ridiculous, and we promise that if you don't find something you like, we'll feel bad about it and be sad. Also, don't forget that we've got robot gift guides going back like five years (here: 2016, 2015, 2014, 2013, 2012), and since we try to mix them up every year, they're great places for even more ideas for robots that are probably way cheaper now than when we first posted about them. And remember: While we provide prices and links to places where you can buy these items, we're not endorsing any in particular, and a little bit of searching may result in better deals (all prices are in U.S. dollars).

Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of various types of cancers better. Tri-matrix factorization is an efficient tool to learn associations between two different entities (e.g., cancer types and pathways in our case) from data. To successfully apply tri-matrix factorization methods to biomedical problems, biological prior knowledge such as pathway databases or protein-protein interaction (PPI) networks, should be taken into account in the factorization model. However, it is not straightforward in the Bayesian setting even though Bayesian methods are more appealing than point estimate methods, such as a maximum likelihood or a maximum posterior method, in the sense that they calculate distributions over variables and are robust against overfitting. We propose a Bayesian (semi-)nonnegative matrix factorization model for human cancer genomic data, where the biological prior knowledge represented by a pathway database and a PPI network is taken into account in the factorization model through a finite dependent Beta-Bernoulli prior. We tested our method on The Cancer Genome Atlas (TCGA) dataset and found that the pathways identified by our method can be used as a prognostic biomarkers for patient subgroup identification.

A United Nations meeting in Geneva earlier this month on lethal autonomous weapons systems (LAWS) was derailed when Russia said they would not adhere to any prohibitions on killer robots, according to Defense One. The U.N. meeting appeared to be undermined both by Russia's disinterest in it and the framework of the meeting itself. Member nations attempted to come in and define what LAWS' systems would be, and what restrictions could be developed around autonomous war machines, but no progress was made. In a statement, Russia said that the lack of already developed war machines makes coming up with prohibitions on such machines difficult. "According to the Russian Federation, the lack of working samples of such weapons systems remains the main problem in the discussion on LAWS…this can hardly be considered as an argument for taking preventive prohibitive or restrictive measures against LAWS being a by far more complex and wide class of weapons of which the current understanding of humankind is rather approximate," read the statement.

Hyderabad, 15th November 2017: Times Professional Learning recently conducted a Deep Learning Workshop at Hyderabad, in association with its technology partner NVIDIA. The one day workshop got a good response from the technology enthusiasts of Hyderabad. The instructor-led NVIDIA Deep Learning Institute (DLI) Master Class on deep learning helped students and professionals understand various aspects of Machine Learning and Artificial Intelligence (AI). NVIDIA Deep Learning Institute (NVDLI) focuses on an instructor-led training for developers, data scientists and researchers. It was the first among a series of workshops to be conducted all across India at major cities such as Mumbai, New Delhi, Chennai, Pune and Bengaluru.

Artificial intelligence and machine learning are two phrases that are thrown around a lot in the tech world these days. It has gotten the point where every company has to say they're an AI company, even if they really don't have AI capabilities, just to be taken seriously. The problem is that the best data scientists all want to work for the same few companies: Google, Facebook or Apple. So what are the smaller companies to do? That's the problem that H2O.ai is solving.

The U.S. holds an enviable lead in pushing artificial-intelligence technology out of labs and into real-world applications. Thank companies like Alphabet (GOOGL), Facebook (FB) and Apple (AAPL) for that.

Last month, the world was introduced to Sophia, the first robot to receive citizenship in the Kingdom of Saudi Arabia. Millions of people tuned into CNBC as Sophia was interviewed by Andrew Ross Sorkin, responding to his questions with all the poise and perceived emotional intelligence of a confident, educated human woman. While it's still unclear if Sophia gaining citizenship was a PR stunt or not, the sophistication, magnitude--and repercussions--of the technology are indisputable. And, of course, this explosive surge of technology begs the question, will artificial intelligence take our jobs? Most AI experts say the short answer is, yes, but there is a caveat.

The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom, and will focus more on practical skills rather than on theoretical knowledge. KDNuggets will continue to challenge the curious mind by publishing articles like 10 Algorithms that Machine Learning Engineers Should Know .

One of the most interesting sessions at the recent Dreamforce conference was the fireside chat with IBM CEO Ginni Rometty, an hour long discussion that covered a lot of ground. In the mix were some compelling views on AI, both in terms of its potential for good, but also of the responsibilities that came with such new tech. Time constraints restricted the amount of discussion that could take place at Dreamforce, so it was good to see many of the topics and concerns alluded to by Rometty picked up in more detail by Doctor John Kelly, Head of Cognitive Solutions and IBM Research, at the 21st Annual Credit Suisse Technology, Media & Telecoms Conference this week. Cognitive is IBM's preferred terminology for a range of tech that covers AI, machine-learning et al. Kelly took time out to define some of IBM's nomenclatures and categorisations, beginning with Narrow AI: This is machine learning to simply identify patterns of things. In the case of imaging, lots of companies use very narrow AI to sort through the pictures that you store on the cloud or on your phone, as an example, to identify family members or cats or pets or whatever, or an industry to find defects in products like pharmaceuticals, that's very narrow, reinforced learning technology.

Stunning VR footage that could be taken straight from the latest video game has revealed the United Arab Emirates' vision for its planned Martian colony where robots live alongside humans. An immersive 360 experience of the futuristic city was released by the Gulf state's government, giving the most detailed view yet of its city. The release is part of Dubai's Mars 2117 Strategy, which seeks to build the first settlement on Mars in the next 100 years. Stunning VR footage that could be taken straight from the latest video game has revealed the United Arab Emirates' vision for its planned Martian colony. There are some fairly major reasons why we haven't got anyone on Mars yet: The video begins with a greeting from a holographic representative of the United Government of Mars, welcoming you to your second home.