Rollin' Justin is the name of an AI humanoid bot that was developed by the German space agency DLR. It was created for future Mars missions, and will both aid astronauts as well as perform various household tasks. Standing 1.9m tall and weighing 200kg, Rollin' Justin features 51 degrees of freedom and a 20kg loading capacity. In addition to the four-finger hands and compliant light-weight arms, it is equipped with four main design features: mechatronic design, compliant whole-body manipulation, mobile manipulation and autonomous task planning and execution. The bot can even achieve independent thinking using advanced AI technology, allowing its to inspect its surroundings, and take on jobs as detailed as cleaning solar panels.

About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using the command sudo apt-get install awscam on the Ubuntu terminal. For this tutorial, you need the MXNet version 0.12. You can update the MXNet version by using the command sudo pip3 install mxnet 0.12.1.

These are some of the highlights from the Day 2 of the Age of AI Conference, held on January 31 and February 1, 2018, at the Regency Ballroom in San Francisco. The Conference owes its origins in the San Francisco Artificial Intelligence meetup that Emil Mikhailov started for the interested ones to learn, network and share. The community now boasts of 4,700 members and has previously hosted heavyweights like Andrew Ng and the Nvidia CEO Jensen Huang. The Regency Ballroom boasts good location and acoustics. The best part was the technical focus of the Conference, well punctuated with some'global minima' but thought-provoking touches.

Scientists from Google and Verily have developed a new Artificial Intelligence (AI) algorithm that can determine a person's risk of heart attack through their retinal scan.

Earlier this month, I had the exciting opportunity to moderate a discussion between Professors Yann LeCun and Christopher Manning, titled "What innate priors should we build into the architecture of deep learning systems?" The event was a special installment of AI Salon, a discussion series held within the Stanford AI Lab that often features expert guests. This discussion topic – about the structural design decisions we build into our neural architectures, and how those correspond to certain assumptions and inductive biases – is an important one in AI right now. In fact, last year I highlighted "the return of linguistic structure" as one of the top four NLP Deep Learning research trends of 2017. On one side, Manning is a prominent advocate for incorporating more linguistic structure into deep learning systems.

One quarter of customer service and support operations will integrate chatbot technology – otherwise known as virtual customer assistant (VCA), across engagement channels by 2020, up from less than 2% last year, says analyst firm Gartner. According to research by Gartner, organizations report a reduction of up to 70 percent in call, chat or email inquiries after implementing a VCA. They also report increased customer satisfaction and a 33% saving per voice engagement. And organizations realize this, with more than half of those investing in VCAs doing so for customer service to tap into the advantages of automated self-service and the ability to escalate to a human agent in complex situations. "As more customers engage on digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks. This is underpinned by improvements in natural-language processing, machine learning and intent-matching capabilities," said Gene Alvarez, managing vice president at Gartner at the Gartner Customer Experience Summit in Tokyo earlier this week.

Automation and personalization, although very different in meaning, are very closely connected and go hand in hand with each other. A good question to ask ourselves in this context is – what happens if one interferes with the other? When we talk about the development of businesses, personalization, automation, tools and devices that will take the way we do business to a whole new level, we realize that there is a need to automate personalization more, while customizing automation. This is where automation vs. personalization issue arises. In the context of Artificial Intelligence and Human Resources, automation and personalization are intertwined as well as these two areas.

Alongside news of Google Assistant's forthcoming multilingual support and the addition of more languages this year, Google also announced this morning its smart assistant would soon be gaining two new features: Routines and location-based reminders. Google has been promising Routines were in the works for some time. The feature, which lets Google Assistant users string together multiple commands, was first announced back in October 2017. With Routines, you can create personalized commands and responses – for example, saying "OK Google, I'm home," could turn on the lights, adjust the thermostat, and play some music. It's a feature that rival Alexa announced in September 2017, and launched the following month. Google is playing catch up here, but it's doing it quickly.

A 100-page report written by artificial intelligence experts from industry and academia has a clear message: Every AI advance by the good guys is an advance for the bad guys, too. The paper, titled "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation," calls this the "dual-use" attribute of AI, meaning the technology's ability to make thousands of complex decisions every second could be used to both help or harm people, depending on the person designing the system. The experts considered the malicious uses of AI that either currently exists or could be developed over the next five years, and broke them out into three groups: digital, physical, and political. Here is a selected list of the potential harms discussed: The report paints a bleak picture of our potential future, especially since the timeframe is a mere five years. But it doesn't mean we're resigned to dystopia.