Artificial intelligence and robotics have enjoyed a resurgence of interest, and there is renewed optimism about their place in our future. But what do they mean for us? How plausible is human-like artificial intelligence? It is 100% plausible that we'll have human-like artificial intelligence. I say this even though the human brain is the most complex system in the universe that we know of. But there are also no physical laws we know of that would prevent us reproducing or exceeding its capabilities. Popular AI from Issac Asimov to Steven Spielberg is plausible.

Refined stellar yardstick helps astronomers improve stellar evolution models. A globular cluster as seen by the Hubble telescope. Researchers working on Search for Extraterrestrial Intelligence (SETI) efforts hunt for the same thing that their predecessors sought for decades--a sign that life arose, as Carl Sagan would say, on another humdrum planet around another humdrum star and rose up into something technologically advanced. It could happen any day. A weird, brief flash in the night sky.

COCIR welcomes the inception impact assessment by the European Commission on ethical and legal requirements for Artificial Intelligence (AI) and the opportunity to provide feedback. Continuing our engagement in this area, and following the earlier consultation on the AI White Paper, COCIR is pleased to share its experience and expertise on the use of AI within healthcare. COCIR and its members have recently published a comprehensive in-depth analysis of Artificial Intelligence in Medical Device Legislation. The document provides a thorough analysis of the legal requirements applicable to AI-based medical devices. Based on this analysis COCIR sees no need for novel regulatory frameworks for AI-based medical devices, because the requirements of the EU Medical Device Regulation4 (MDR) in combination with provisions of the General Data Protection Regulation (GDPR) are adequate to ensure excellence and trust in AI in line with European values.

Portland, Oregon has become the first city to declare private use of facial recognition tech unlawful. The city's legislators have unanimously voted to pass a sweeping new law that bans both the private and public use of facial recognition. While other cities such as Oakland, San Francisco, and Boston have already prohibited government agencies from employing facial surveillance technology, Portland is the first that has outlawed its private use. "Portlanders should never be in fear of having their right of privacy be exploited by either their government or by a private institution," said Mayor Ted Wheeler (via OneZero) during the hearing. "All Portlanders and frankly all people are entitled to a city government that will not use technology with a demonstrated racial and gender bias which endangers personal privacy."

The pandemic is helping to usher in a new era of food-production forecasts that rely more on satellite data and artificial intelligence and less on information gathered by people. The crop world, including major trading houses and statisticians at the U.S. Department of Agriculture, has long depended on scouts trudging through fields to count corn kernels and soybean pods. But travel restrictions and new virus safety measures have cut participation in field tours at a time of increasing scrutiny over food security. "Covid-19 is disrupting agricultural supply chains in developing countries, and observers on the ground can no longer report on crop conditions," said Lillian Kay Petersen, a student at Harvard University. She won the top prize of this year's Regeneron Science Talent Search, a 79-year-old competition for high school students held by the Society for Science and the Public, for her model that uses daily satellite images to predict crop yields in Africa.

The upcoming US presidential election seems set to be something of a mess--to put it lightly. Covid-19 will likely deter millions from voting in person, and mail-in voting isn't shaping up to be much more promising. This all comes at a time when political tensions are running higher than they have in decades, issues that shouldn't be political (like mask-wearing) have become highly politicized, and Americans are dramatically divided along party lines. So the last thing we need right now is yet another wrench in the spokes of democracy, in the form of disinformation; we all saw how that played out in 2016, and it wasn't pretty. For the record, disinformation purposely misleads people, while misinformation is simply inaccurate, but without malicious intent.

NASA has announced it wants to buy Moon rocks from private companies in a bid to kick start lunar mining operations. The space agency is taking proposals from companies on how they will collect rock from the Moon, using robotic surface rovers. NASA then plans to purchase the samples in amounts of 50 to 500 grams for between $15,000 and $25,000. The collection of lunar rock and transfer of ownership to NASA is a proof of concept for conducting space commerce on the Moon, NASA said. In other words, the initiative will help to establish the early principles for how mining operations between'space entrepreneurs' will work, which could help sustain future astronaut missions.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.

Legal argumentation is a vital cornerstone of justice, underpinning an adversarial form of law, and extensive research has attempted to augment or undertake legal argumentation via the use of computer-based automation including Artificial Intelligence (AI). AI advances in Natural Language Processing (NLP) and Machine Learning (ML) have especially furthered the capabilities of leveraging AI for aiding legal professionals, doing so in ways that are modeled here as CARE, namely Crafting, Assessing, Refining, and Engaging in legal argumentation. In addition to AI-enabled legal argumentation serving to augment human-based lawyering, an aspirational goal of this multi-disciplinary field consists of ultimately achieving autonomously effected human-equivalent legal argumentation. As such, an innovative meta-approach is proposed to apply the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR) to the maturation of AI and Legal Argumentation (AILA), proffering a new means of gauging progress in this ever-evolving and rigorously sought domain.

The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.