Human-level intelligence is familiar in biological "hardware" – it happens inside our skulls. Technology and science are now converging on a possible future where similar intelligence can be created in computers. While it is hard to predict when this will happen, some researchers suggest that human-level AI will be created within this century. Freed of biological constraints, such machines might become much more intelligent than humans. What would this mean for us?

Related: Robot revolution: rise of'thinking' machines could exacerbate inequality As a senior adviser to Hillary Clinton, Alec Ross travelled the world with the remit of cataloguing the best examples of innovation the human race has to offer. His trips took him to Korea, the Congo and Silicon Valley (and far enough overall he has calculated, to take him from the Earth to the moon twice, with a side trip from the US to New Zealand), and left him with a concern that the rate of change could leave many behind. From robots entering the workforce and leading to the very real prospect of redundancy within a decade for the million employees of Taiwan's electronics manufacturing giant Foxconn to genetic engineering unleashing the possibility of designer babies, the power of technology to reshape the world is reaching historic levels. But the people who have the most to lose from those changes are often the ones who get the least warning. That, says Ross, was his motivation for writing The Industries of the Future, which looks at six of the biggest waves of change about to hit the world.

Medical, Agricultural, and Computing Science Researchers at the University of Alberta and the AICML have developed a new test to detect E. coli. The PFM Scheduling Services website is now available here. The AICML has just released a new video talking about what machine learning is and what it can do for you. AICML researcher Patrick Pilarski recently gave a talk at TEDx Edmonton. The Critterbot Project is an initiative of the Reinforcement Learning and Artificial Intelligence (RLAI) lab at the University of Alberta.

What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun--but not "he" or "she." The algorithm went on to pick the funniest captions for thousands of the New Yorker's cartoons, and in most cases, it matched the intuition of its editors. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz. Google released the word2vec tool, and Facebook followed by publishing their speed optimized deep learning modules.

"The development of a special military robot is one of the priorities of military construction in Russia," the Russian daily newspaper Komsomolskaya Pravda reported recently. The purpose of Iron Man, the newspaper continued, is to "replace the person in the battle or in emergency areas where there is a risk of explosion, fire, high background radiation, or other conditions that are harmful to humans." Experts have known that Russia has been trying in recent years to match the US and China in the development of robots, drones, and other war machines that are potentially autonomous. Today, those machines are remotely controlled. Iron Man and other recent developments illustrate how they're making progress.

PAPIs '15, the 2nd International Conference on Predictive APIs and Applications, took place in Sydney, Australia and featured 4 research presentations. The corresponding papers were compiled into proceedings that were published in the Journal of Machine Learning Research (Volume 50 of the Workshop & Conference Proceedings series; you can also download the whole proceedings in a single pdf here). The first paper of these proceedings gives us a behind-the-scenes look at Microsoft Azure ML, an MLaaS environment for authoring predictive models, experimenting with them, running them on a cloud infrastructure and publishing them as web APIs. The Azure ML team presents design principles, challenges encountered and lessons learnt while building the platform. While it is common for ML practitioners to measure models' performance via predictions' accuracy, the second paper of these proceedings by Brian Gawalt of Upwork focuses on concerns of software engineers who are in charge of deploying in production and scalability: models' throughput and response time.

Researchers in Germany are developing a way for robots to feel pain, in the hopes that doing so will enable them to better protect humans. The researchers, from Leibniz University of Hannover, are working on an "artificial robot nervous system to teach robots how to feel pain," IEEE Spectrum reports, and presented their project at a robotics and automation conference in Sweden last week. Under the system, robots would identify pain and quickly respond to avoid further damage to their parts. Johannes Kuehn, one of the researchers working on the system, says that enabling robots to feel and react to pain could help mitigate damage in the same way that humans sense pain to protect themselves. "Pain is a system that protects us," Kuehn tells IEEE Spectrum.

A team of CMU researchers led by Associate Professor Anupam Datta have developed new measurement methods that provide important insight into how machine-learning algorithms make decisions about things like credit applications, job opportunities and medical diagnoses. Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University researchers could provide important insights to this process. Was it a person's age, gender or education level that had the most influence on a decision? Was it a particular combination of factors?

The supplier for Apple and Samsung is leading a new push for automated manufacturing. According to reports, the world's largest electronics manufacturer Foxconn has replaced around 60,000 human factory workers with machines. Or, as a government publicist for the city of Kunshan told the South China Morning Post, the factory "reduced employee strength from 110,000 to 50,000 thanks to the introduction of robots. It has tasted success in reduction of labour costs." Although Foxconn confirmed to the BBC that it was working to automate much of its manufacturing operations, the company denied that the new robotic assembly line would mean fewer jobs for humans. Instead, the company says it is simply using the machines to "replace repetitive tasks previously done by employees" while allowing those employees to focus on more valuable parts of the manufacturing process like R&D and quality control.

If you're one of the 38 million American travelers this Memorial Day weekend, or enjoying one of the 800 hot dogs consumed per second during this holiday, then take a step back, relax and enjoy the week's top technology news! The question of human sustainability and sufficient resources on our planet has served as the basis for many science fiction movies. But unlike Hollywood's'Interstellar' film, our answer might be right here on Earth. AI is showing potential to completely revolutionize farming, and with it keep humanity's food supply coming. What if we could go past what we know as binary computing?