Code Talkers

Communications of the ACM

When Tavis Rudd decided to build a system that would allow him to write computer code using his voice, he was driven by necessity. In 2010, he tore his rotator cuffwhile rock-climbing, forcing him to quit climbing while the injury healed. Rather than sitting idle, he poured more of his energy into his work as a self-employed computer programmer. "I'd get in the zone and just go for hours," he says. Whether it was the increased time pounding away at a keyboard or the lack of other exercise, Rudd eventually developed a repetitive strain injury (RSI) that caused his outer fingers to go numb and cold, leaving him unable to type/code without pain.

AI that has learnt how to play death metal music 24/7 on YouTube

Daily Mail

Artificial intelligence has been used to generate an endless stream of death metal music which will play on YouTube for 24 hours a day. The creation comes from two US programmers who built a virtual band known as'Dadabot'. It creators are now letting the technology play forever via a live stream called'Relentless Doppelganger' on the video-sharing platform. An AI system has been programmed to generate heavy metal music on YouTube 24 hours a day, with the aim to do so until'infinity'. Dadabot, and its continual supply of music, was trained using a large amount of music from Canadian death metal band Archspire.

How GitHub Is Helping Overworked Chinese Programmers


Two Chinese software developers are trying to harness the power of open source software to improve working conditions for coders. Last weekend, Katt Gu and Suji Yan, published the "Anti-996 License," which requires any company that uses the project's software to comply with local labor laws as well as International Labour Organization standards, including the right for workers to collectively bargain and a ban on forced labor. The license is part of the growing Anti-996 Movement in China, which refers to a common schedule of working from 9 am to 9 pm, six days a week. This grueling schedule is allegedly widespread in the Chinese tech startup industry, according to a story in the South China Morning Post last month. Last week, one or more anonymous activists launched a website called 996.ICU, detailing Chinese labor laws that a 996 schedule may violate, including provisions that generally limit work to 44 hours a week and require overtime pay.

Untold History of AI: Why Alan Turing Wanted AI Agents to Make Mistakes

IEEE Spectrum Robotics Channel

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In 1950, at the dawn of the digital age, Alan Turing published what was to be become his most well-known article, "Computing Machinery and Intelligence," in which he poses the question, "Can machines think?"

Can we stop robots outsmarting humanity?

The Guardian

It began three and a half billion years ago in a pool of muck, when a molecule made a copy of itself and so became the ultimate ancestor of all earthly life. It began four million years ago, when brain volumes began climbing rapidly in the hominid line. In less than thirty years, it will end. Jaan Tallinn stumbled across these words in 2007, in an online essay called Staring into the Singularity. The "it" was human civilisation. Humanity would cease to exist, predicted the essay's author, with the emergence of superintelligence, or AI, that surpasses human-level intelligence in a broad array of areas. Tallinn, an Estonia-born computer programmer, has a background in physics and a propensity to approach life like one big programming problem.

Why Not Appoint an Algorithm to Your Corporate Board?


Though Elon Musk has famously warned humanity about the dangers of artificial intelligence, his shareholders might be well-served by having an algorithm on Tesla's board of directors. In recent years, Tesla has become a cautionary tale for how difficult it is for part-time directors to oversee charismatic, strong-willed CEOs--especially ones who are the founding visionaries of their companies. Given how Elon Musk has landed the company in hot water with the Securities and Exchange Commission with his erratic tweets and mocking disregard for the regulatory regime dictating the proper behavior of a publicly traded company, it's little wonder that Tesla's board has been accused of being "asleep at the wheel." Perhaps their seeming unwillingness to rein him in is due to the Tesla directors' personal loyalty to Musk. Or maybe they simply don't want to spend the time to "preapprove" Musk's tweets about the company, especially with the less conventional hours and fast pace the CEO keeps.

Automated machine learning streamlines model building


Data science platforms are gunning toward automation. With the widespread release and popularity of Google Cloud AutoML, DataRobot Inc. tools and other automated machine learning platforms, analysts, businesses and users are beginning to tap into the technology and the rapidity of automation. In this Q&A, Mike Gualtieri, vice president and principal analyst at Forrester Research, outlines the state of automated machine learning platforms and their use cases. Editor's note: The following has been edited for clarity and brevity. What are some key capabilities of automated machine learning platforms?

A Clear Definition of Machine Learning GovLoop


There's a lot of buzz about machine learning in government today, given its potential to improve operations, cut costs and produce better program outcomes. But what exactly is it? Machine learning, or ML, is a collection of algorithms and mathematical models used by computer systems to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as training data, in order to make predictions or decisions, without being explicitly programmed to perform the task. Put more simply, machine learning is when computer systems use data to train themselves to perform certain actions – rather than requiring humans to do the same work.

Toward Imitating Visual Attention of Experts in Software Development Tasks Artificial Intelligence

Expert programmers' eye-movements during source code reading are valuable sources that are considered to be associated with their domain expertise. We advocate a vision of new intelligent systems incorporating expertise of experts for software development tasks, such as issue localization, comment generation, and code generation. We present a conceptual framework of neural autonomous agents based on imitation learning (IL), which enables agents to mimic the visual attention of an expert via his/her eye movement. In this framework, an autonomous agent is constructed as a context-based attention model that consists of encoder/decoder network and trained with state-action sequences generated by an experts' demonstration. Challenges to implement an IL-based autonomous agent specialized for software development task are discussed in this paper.

The Only Unconventional Guide To Machine Learning


First of all, I would like to throw a very warm welcome to all of you! The topic we are going to cover in this section is about machine learning, and I am going to talk about the various aspects related to machine learning. We are going to cover what machine learning actually is in a very easy yet conceptualized way. Now speaking about machine learning the term, AI Development and machine learning is closely related and it's not wrong to say that the abstraction level between machine learning and artificial intelligence has a silver lining in between. When it comes to machine learning what most of the people saying is the same old Terminator movie. You think that there is going to be some Tx 9000 machine that is going to come up from the future and destroy the entire humanity and you start panicking.