Federated learning (FL) is a distributed machine learning approach involving multiple clients collaboratively training a shared model. Such a system has the advantage of more training data from multiple clients, but data can be non-identically and independently distributed (non-i.i.d.). Privacy and integrity preserving features such as differential privacy (DP) and robust aggregation (RA) are commonly used in FL. In this work, we show that on common deep learning tasks, the performance of FL models differs amongst clients and situations, and FL models can sometimes perform worse than local models due to non-i.i.d. data. Secondly, we show that incorporating DP and RA degrades performance further. Then, we conduct an ablation study on the performance impact of different combinations of common personalization approaches for FL, such as finetuning, mixture-of-experts ensemble, multi-task learning, and knowledge distillation. It is observed that certain combinations of personalization approaches are more impactful in certain scenarios while others always improve performance, and combination approaches are better than individual ones. Most clients obtained better performance with combined personalized FL and recover from performance degradation caused by non-i.i.d. data, DP, and RA.

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.

AI systems sometimes run amok. One chatbot, designed by Microsoft to mimic a teenager, began spewing racist hate speech within hours of its release online. Microsoft immediately took the bot down. Another system, which Amazon designed to help its recruiting efforts but ultimately didn't release, inadvertently discriminated against female applicants. Other so-called "smart" systems have led to false arrests, biased bail amounts for criminal defendants, and even fatal car crashes. Experts expect to see more cases of problematic AI as organizations increasingly implement intelligent technology, sometimes doing so without adopting the proper governance in place.

More than 90 policy groups from the US and around the world signed an open letter urging Apple to drop its plan to have Apple devices scan photos for child sexual abuse material (CSAM). This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "The undersigned organizations committed to civil rights, human rights, and digital rights around the world are writing to urge Apple to abandon the plans it announced on 5 August 2021 to build surveillance capabilities into iPhones, iPads, and other Apple products," the letter to Apple CEO Tim Cook said. "Though these capabilities are intended to protect children and to reduce the spread of child sexual abuse material (CSAM), we are concerned that they will be used to censor protected speech, threaten the privacy and security of people around the world, and have disastrous consequences for many children." The Center for Democracy and Technology (CDT) announced the letter, with CDT Security and Surveillance Project codirector Sharon Bradford Franklin saying, "We can expect governments will take advantage of the surveillance capability Apple is building into iPhones, iPads, and computers.

If your company gave notice, it likely came in one of the many forms you signed when you accepted the job, Kropp said. But if you get in trouble for something your employer catches you doing while monitoring you remotely, you likely don't have recourse. Almost all types of employee surveillance are entirely legal, according to Emory Roane, privacy counsel at the nonprofit organization Privacy Rights Clearinghouse.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. We chat with Lofred Madzou about AI as a journey to understand ourselves through smart machines, scepticism about wholesale job loss, understanding that "you are not your data", dissecting the European proposal for AI regulation, examples of types of AI activities under regulation, the spirit of the regulation – human rights-centric, risk-based approaches, infringement exposition and compliance… Lofred Madzou is a Project Lead for AI at the World Economic Forum, where he oversees global and multistakeholder AI policy projects. He is also a research associate at the Oxford Internet Institute where he investigates various methods to audit AI systems. Before joining the Forum, he was a policy officer at the French Digital Council, where he advised the French Government on technology policy. Most notably, he has co-written chapter five of the French AI National Strategy, entitled "What Ethics for AI?".

"History is more or less bunk."--Henry The law of intellectual property relating to artificial intelligence and its products is both already established in the existing law, and will have to be invented as AI plays our games, writes our books, plays, and music and, with expert software, puts lawyers out of business. Perhaps its complexities will be so difficult that we will have to resort to AI judges. However, another sage tells us, "It's hard to make predictions, especially about the future." This includes all aspects of AI.

Michael Williams' wife pleaded with him to remember their fishing trips with the grandchildren, how he used to braid her hair, anything to jar him back to his world outside the concrete walls of Cook County Jail. His three daily calls to her had become a lifeline, but when they dwindled to two, then one, then only a few a week, the 65-year-old Williams felt he couldn't go on. He made plans to take his life with a stash of pills he had stockpiled in his dormitory. Williams was jailed last August, accused of killing a young man from the neighborhood who asked him for a ride during a night of unrest over police brutality in May. But the key evidence against Williams didn't come from an eyewitness or an informant; it came from a clip of noiseless security video showing a car driving through an intersection, and a loud bang picked up by a network of surveillance microphones. Prosecutors said technology powered by a secret algorithm that analyzed noises detected by the sensors indicated Williams shot and killed the man. "I kept trying to figure out, how can they get away with using the technology like that against me?" said Williams, speaking publicly for the first time about his ordeal. Williams sat behind bars for nearly a year before a judge dismissed the case against him last month at the request of prosecutors, who said they had insufficient evidence.

Society could soon see transformative artificial intelligence (TAI). Models of competition for TAI show firms face strong competitive pressure to deploy TAI systems before they are safe. This paper explores a proposed solution to this problem, a Windfall Clause, where developers commit to donating a significant portion of any eventual extremely large profits to good causes. However, a key challenge for a Windfall Clause is that firms must have reason to join one. Firms must also believe these commitments are credible. We extend a model of TAI competition with a Windfall Clause to show how firms and policymakers can design a Windfall Clause which overcomes these challenges. Encouragingly, firms benefit from joining a Windfall Clause under a wide range of scenarios. We also find that firms join the Windfall Clause more often when the competition is more dangerous. Even when firms learn each other's capabilities, firms rarely wish to withdraw their support for the Windfall Clause. These three findings strengthen the case for using a Windfall Clause to promote the safe development of TAI.

The machine learning system is part of HOLO AITM, Surgalign's core technology in artificial intelligence and augmented reality. DEERFIELD, Ill., Aug. 19, 2021 – Surgalign Holdings, Inc., (NASDAQ: SRGA) a global medical technology company focused on elevating the standard of care by driving the evolution of digital surgery, today announced that the United States Patent and Trademark Office (USPTO) recently issued a patent covering a machine learning system for automated segmentation of a three-dimensional bony structure in a medical image. The granted patent expands and further strengthens the company's HOLO AI technology portfolio. "This patent is a foundational element of how we harness technology and data to power our digital surgery platform," said Terry Rich, Surgalign's president and chief executive officer. "While'artificial intelligence' and'machine learning' have become buzzwords that are often misused, misrepresented, and misunderstood, at Surgalign AI is a core competency and a key element of our efficient and highly valuable approach to improving patient's lives."