Do you have a great idea on how to put a spotlight on the impact of AI on freedom of expression? Or perhaps an innovative initiative for tackling the challenges brought about by AI? In the framework of the #SAIFE project, the OSCE RFoM is looking to fund a limited number of innovative ideas to put a spotlight on AI and freedom of expression. Supported activities can include, but are not limited to, the development of tools to counter freedom of expression challenges posed by AI; educational and awareness-raising activities; podcasts; documentaries; mini-series; exhibitions; and storytelling. For more information, see the call for proposals here.

Due to the facial identification features in today's smart devices and laptops, many people are familiar with the concept of facial recognition technology. However, they may not know exactly how it works or realize how many software programs and applications use this technology to function. Tech experts aren't the only ones discussing the current state and potential of facial recognition technology; politicians, human rights advocates and others are examining both its possibilities and its troubling drawbacks, and it's wise for the public to stay informed about both the pros and cons. For a better understanding of how facial recognition works, we turned to the experts of Forbes Technology Council. Below they share 12 features of facial recognition technology everyone should know about.

Artificial Intelligence (AI) is the ability of a computer or computer-controlled products to perform tasks that only intelligent beings are able to do. The most common use for this term is to categorize projects, systems, or products that are able to develop intellectual processes normally performed by humans, such as reasoning, analyzing, generalizing, learning from previous experiences, etc. Business analysts expect that the AI industry invade business processes around the world, estimating that the AI market will grow at an annual rate of 20%. In the past four years, there has been a 270% increase in organizations that have recently implemented AI products. Additionally, analysts estimate that 80% of emerging technologies will have an AI component. The recent disruption of this sector has also reached the intellectual property sector.

An expanding field of substantive interest for the theory of the law and the practice-of-law entails Legal Sentiment Analysis and Opinion Mining (LSAOM), consisting of two often intertwined phenomena and actions underlying legal discussions and narratives: (1) Sentiment Analysis (SA) for the detection of expressed or implied sentiment about a legal matter within the context of a legal milieu, and (2) Opinion Mining (OM) for the identification and illumination of explicit or implicit opinion accompaniments immersed within legal discourse. Efforts to undertake LSAOM have historically been performed by human hand and cognition, and only thinly aided in more recent times by the use of computer-based approaches. Advances in Artificial Intelligence (AI) involving especially Natural Language Processing (NLP) and Machine Learning (ML) are increasingly bolstering how automation can systematically perform either or both of Sentiment Analysis and Opinion Mining, all of which is being inexorably carried over into engagement within a legal context for improving LSAOM capabilities. This research paper examines the evolving infusion of AI into Legal Sentiment Analysis and Opinion Mining and proposes an alignment with the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR), plus provides additional insights regarding AI LSAOM in its mechanizations and potential impact to the study of law and the practicing of law.

Emerging technology is slowly finding a place in developing countries for its potential to plug gaps in ailing public service systems, such as healthcare. At the same time, cases of bias and discrimination that overlap with the complexity of algorithms have created a trust problem with technology. Promoting transparency in algorithmic decision-making through explainability can be pivotal in addressing the lack of trust with medical artificial intelligence (AI), but this comes with challenges for providers and regulators. In generating explainability, AI providers need to prioritise their accountability to patient safety given that the most accurate of algorithms are still opaque. There are also additional costs involved. Regulators looking to facilitate the entry of innovation while prioritising patient safety will need to look into ascertaining a reasonable level of explainability considering risk factors and the context of its use, and adaptive and experimental means of regulation. Artificial intelligence (AI) models across the globe have come under the scanner over ethical issues; for instance, Amazon's hiring algorithm reportedly discriminates against women,[1] and there is evidence of racial bias in the facial recognition software used by law enforcement in the United States (US).[2] While biased AI has various implications, concerns around the use of AI in ethically sensitive industries, such as healthcare, justifiably require closer examination. Medical AI models have become more commonplace in clinical and healthcare settings due to their higher accuracy and lower turnaround time and cost in comparison to non-AI techniques.

The research company Capgemini investigated how artificial intelligence affects sectoral life. Research shows that 60 percent of companies that make decisions on artificial intelligence have gone through a legal review process. The research also reveals what kind of artificial intelligence systems consumers want. Capgemini, a consultancy company based in France, conducted a research on the reflection of artificial intelligence on the sectors. Research has revealed that 90 percent of companies that start using artificial intelligence-supported systems face various ethical problems.

Once in a while, a young company will claim it has more experience than would be logical -- a just-opened law firm might tout 60 years of legal experience, but actually consist of three people who have each practiced law for 20 years. The number "60" catches your eye and summarizes something, yet might leave you wondering whether to prefer one lawyer with 60 years of experience. There's actually no universally correct answer; your choice should be based on the type of services you're looking for. A single lawyer might be superb at certain tasks and not great at others, while three lawyers with solid experience could canvas a wider collection of subjects. If you understand that example, you also understand the challenge of evaluating AI chip performance using "TOPS," a metric that means trillions of operations per second, or "tera operations per second."

Artificial intelligence (AI) is set to upend nearly every industry. It's a technology that will deliver astronomical gains in productivity, dramatic cost reductions, and tremendous advances in research and development. With AI set to increase global GDP by more than $15.7 trillion by 2030, it can be easy to assume that the technology can be nothing but an unfettered good. That would be a dangerous mistake. AI, like any technology, can have detrimental personal, societal, and economic effects: some common concerns include the fact it provides tools that can be exploited by criminals to compromise the cyber security of individuals and organisations, or that the predictive abilities of AI raise a swathe of privacy concerns.

What in the name of Paypal and/or Palantir did you just say about me, you filthy degenerate? I'll have you know I'm the Crown Prince of Silicon Valley, and I've been involved in numerous successful tech startups, and I have over $1B in liquid funds. I've used that money to promote heterodox positions on human enhancement, control political arenas, and am experimenting with mind uploading. I'm also trained in classical philosophy and was recently ranked the most influential libertarian in the world by Google. You are nothing to me but just another alternative future. I will wipe you out with a precision of simulation the likes of which has never been seen before, mark my words.

OpenAI's new immensely convincing language generator, GPT-3, recently demonstrated its rhetorical prowess when it argued the case for why it's harmless. Now, research scientist Janelle Shane has used the tool to generate something a bit more lighthearted. Namely, ideas on how to make nuclear waste sites safe for thousands upon thousands of years. Are you not terrified and repulsed?? I prompted GPT-3 with some human proposals for marking a nuclear waste site, in a way that will still be forbidding millennia from now.https://t.co/3v8uPJ98mo