AI has long been enabling innovation, with both big and small impacts. From AI-generated music, to enhancing the remote fan experience at the U.S. Open, to managing coronavirus patients in hospitals, it seems like the future is limitless. But, in the last few months, organizations from all sectors have been met with the realities of both Covid-19 and increasing anxiety over social justice issues, which has led to a reckoning within companies about the areas where more innovation and better processes are required. In the AI industry, specifically, organizations need to embrace their role in ensuring a fairer and less-biased world. It's been well-established that machine learning models and AI systems can be inherently biased, some more than others -- a result most commonly attributed to the data being used to train and develop them.

Sometime next April, a 50-foot-long autonomous ship will shake loose the digital bonds of its human controllers, scan the horizon with radar, and set a course westward across the Atlantic. The Mayflower Autonomous Ship won't be taking commands from a human captain like the first Mayflower did during its crossing back in 1620. Instead it will get orders from an "AI captain" built by programmers at IBM. The Mayflower's computing system processes data from 30 onboard sensors and six cameras to help the ship sail across the ocean, obey shipping rules (like how to pass other ships at sea), and control electrical and mechanical systems like the engine and rudder. There won't be anyone on board if something goes wrong, although it does have to send a daily report to a human operator back in the UK.

Researchers are using machine learning algorithms trained on simulations of space debris as part of a key project. With more than 34,000 pieces of junk orbiting around the Earth, their removal is becoming a matter of safety. Earlier this month an old Soviet Parus navigation satellite and a Chinese ChangZheng-4c rocket were involved in a near miss and in September the International Space Station conducted a manoeuvre to avoid a possible collision with an unknown piece of space debris. A project led by ClearSpace-1, a spin off from research lab EPFL in Zurich, will recover the now obsolete Vespa Upper Part, a payload adapter orbiting 660km above the Earth that was once part of the European Space Agency's Vega rocket. The mission, set for 2025, aims to ensure that it re-enters the atmosphere and burns up in a controlled way.

Technology keeps creating challenges for intellectual property law. The infamous case of the "monkey selfie" challenged the notion of not just who owns a piece of intellectual property, but what constitutes a "who" in the first place. Last decade's semi-sentient monkey is giving way to a new "who": artificial intelligence. The rapid rise of AI has forced the legal field to ask difficult questions about whether an AI can hold a patent at all, how existing IP and patent laws can address the unique challenges that AI presents, and what challenges remain. The answers to these questions are not trivial; stakeholders have poured billions upon billions of dollars into researching and developing AI technologies and AI-powered products and services across academia, government, and industry.

Hyperspectral images (HSIs) are a kind of optical remote sensing image with a high spectral resolution. Hyperspectral images (HSIs) have attracted much attention recently as they possess unique properties and contain massive information. The newly developed deep learning methods are applied successfully in HSI classification, achieving higher accuracy than traditional methods. The earlier DL-based HSI classification methods were based on fully connected neural networks, such as stacked autoencoders (SAEs) and recursive autoencoders (RAEs). Therefore, they destroyed the spatial structure information of an HSI as they could only handle one-dimensional vectors.

Space robotics startup GITAI and the Japan Aerospace Exploration Agency (JAXA) are teaming up to produce the world's first robotics demonstration in space by a private company. The new agreement under the JAXA Space Innovation through Partnership and Co-creation (J-SPARC) initiative aims to demonstrate the potential for robots to automate of the processing of specific tasks aboard the International Space Station (ISS). Robotics is altering many aspects of our lives in many fields and one where it is particularly attractive is in the exploration and exploitation of space. Ironically, the great strides made in manned spaceflight since the first Vostok mission lifted off in 1961 have shown that not only is supporting astronauts in orbit challenging and expensive, there are also many tasks, like microgravity experiments, where the human touch isn't the best choice. These tasks often require complex, precise, and subtle movements that demand either a highly specialized and expensive bespoke apparatus or a robot.

However, as IDTechEx has reported previously in its article'AI in Medical Diagnostics: Current Status & Opportunities for Improvement', image recognition AI's current value proposition remains below the expectations of most radiologists. Over the next decade, AI image recognition companies serving the medical diagnostics space will need to test and implement a multitude of features to increase the value of their technology to stakeholders across the healthcare setting. Radiologists have a range of imaging methods at their disposal and may need to utilise more than one to detect signs of disease. For example, X-ray and CT scanning are both used to detect respiratory diseases. X-rays are cheaper and quicker, but CT scanning provides more detail about lesion pathology due its ability to form 3D images of the chest.

How is Machine Learning helping to develop TB drugs? Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology.

An innovative artificial intelligence (AI) tool developed by NASA has helped identify a cluster of craters on Mars that formed within the last decade. The new machine-learning algorithm, an automated fresh impact crater classifier, was created by researchers at NASA's Jet Propulsion Laboratory (JPL) in California -- and represents the first time artificial intelligence has been used to identify previously unknown craters on the Red Planet, according to a statement from NASA. Scientists have fed the algorithm more than 112,000 images taken by the Context Camera on NASA's Mars Reconnaissance Orbiter (MRO). The program is designed to scan the photos for changes to Martian surface features that are indicative of new craters. In the case of the algorithm's first batch of finds, scientists think these craters formed from a meteor impact between March 2010 and May 2012.

Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems. Called the Adversarial ML Threat Matrix, the initiative is an attempt to organize the different techniques employed by malicious adversaries in subverting ML systems. Just as artificial intelligence (AI) and ML are being deployed in a wide variety of novel applications, threat actors can not only abuse the technology to power their malware but can also leverage it to fool machine learning models with poisoned datasets, thereby causing beneficial systems to make incorrect decisions, and pose a threat to stability and safety of AI applications. Indeed, ESET researchers last year found Emotet -- a notorious email-based malware behind several botnet-driven spam campaigns and ransomware attacks -- to be using ML to improve its targeting. Then earlier this month, Microsoft warned about a new Android ransomware strain that included a machine learning model that, while yet to be integrated into the malware, could be used to fit the ransom note image within the screen of the mobile device without any distortion.