A quick search of recent headlines and blog posts suggests there is anxiety surrounding artificial intelligence (AI). One article shouts, "Robots will soon do your Taxes!" Another reads, "Lawyers could be the next profession to be replaced by computers." Those of us involved in technology marketing strategy and communications are struggling to understand what the true impact of AI will be on our respective companies and clients, and on the technology-based products and services they provide. New AI applications in legal research, contracts management, or e-discovery may fundamentally change the value proposition. For those AI solutions, marketers and communications teams must strive to effectively educate prospects and customers on the nature of artificial intelligence, separating the rumors from facts.

It can no longer be denied that Artificial Intelligence is having a growing impact in many areas of human activity. It is helping humans communicate with each other--even beyond linguistic boundaries--, finding relevant information in the vast information resources available on the web, solving challenging problems that go beyond the competence of a single expert, enabling the deployment of autonomous systems, such as self-driving cars or other devices that handle complex interactions with the real world with little or no human intervention, and many other useful things. These applications are perhaps not like the fully autonomous, conscious and intelligent robots that science fiction stories have been predicting, but they are nevertheless important and useful, and most importantly they are real and here today. The growing impact of AI has triggered a kind of'gold rush': we see new research laboratories springing up, new AI start-up companies, and very significant investments, particularly by big digital tech companies, but also by transportation, manufacturing, financial, and many other industries. Management consulting companies are competing in their predictions on how big the economic impact of AI is going to be and governments are responding with strategic planning to see how their countries can avoid staying behind. Although all of this is good news, it cannot be denied that the application of AI comes with certain risks. Several initiatives have been taken in recent years to better understand the risks of AI deployment and came up with legal frameworks, codes of conduct, and value-based design methodologies.

Anthropomorphizing AI is easy to do. In the age of smart assistants like Google Home, Alexa, and Siri, we imagine that these technologies have our best interests at heart. While painting a mental picture of AI, we usually envision machines that think, learn, and come to conclusions as humans do. For a general understanding of the phenomenon, artificial intelligence is described as the intelligence possessed and displayed by machines and technologies as opposed to the one exhibited by humans. For lack of a better term, AI is used as a blanket expression to represent machine learning, cognitive computing, image recognition, and more.

When we think of human trafficking, we often think about the despondent faces of women and children who live in slums all over the world. What if human trafficking is much closer to home than we think? In 2019, Markie Dell, stood on the TEDx stage to recount her experience of being a domestic human trafficking victim. She was an awkward teenager who was groomed by a girl that she befriended at a birthday party. She was subsequently kidnapped, drugged, sexually violated, intimidated at gunpoint into dancing in strip clubs for an entire year.

In this tutorial, I implemented the building blocks of Federated Learning (FL) and trained one from scratch on the MNIST digit data set. Prior to that, I briefly introduced the subject so as to drive home the overall point in the code. If this is your first time learning about FL, I'm sure you will benefit from my recent introductory article of this technology on LinkedIn. Quality data exist as islands on edge devices like mobile phones and personal computers across the globe and are guarded by strict privacy preserving laws. Federated Learning provides a clever means of connecting machine learning models to these disjointed data regardless of their locations, and more importantly, without breaching privacy laws.

Controversial facial recognition firm Clearview AI has been found to have extensive ties to far-right individuals and movements. Clearview AI has come under scrutiny for scraping billions of photos from across the internet and storing them in a database for powerful facial recognition services. Privacy activists criticise the practice as the people in those images never gave their consent. "Common law has never recognised a right to privacy for your face," Clearview AI lawyer Tor Ekeland said recently. "It's kind of a bizarre argument to make because [your face is the] most public thing out there."

As you might expect, this year, many companies use artificial intelligence (AI) and machine learning at the core of their business to deliver innovative products and service offerings. Anyone interested in AI should know about these 14 pioneering businesses.# This London-based company was founded in 2013 and operates under two business units: BenevolentTech's focus is to develop the artificial intelligence platform that will drive innovation by transforming the way scientists access and use the information available to them. BenevolentBio is the division that applies the tech to generate new ideas that will impact human health such as better medicines and research, insights and innovation for rare diseases. With a mission to make law free and understandable, Casetext leverages artificial intelligence technology to help legal researchers find the most relevant cases quickly.

The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not consider feature dependence and are only related to one class. Counterfactual explanation methods are considered promising to explain complex model decisions, since they are associated with a high degree of human interpretability. In this paper, SEDC is introduced as a model-agnostic instance-level explanation method for image classification to obtain visual counterfactual explanations. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. As image classification tasks are typically multiclass problems, SEDC-T is proposed as an alternative method that allows specifying a target counterfactual class. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed. Moreover, concrete examples and experiments illustrate the potential of our approach (1) to obtain trust and insight, and (2) to obtain input for model improvement by explaining misclassifications.

The tech industry is entering a new age, one in which innovation has to be done responsibly. "It's very novel," says Michael Kearns, a professor at the University of Pennsylvania specialising in machine learning and AI. "The tech industry to date has largely been amoral (but not immoral). Now we're seeing the need to deliberately consider ethical issues throughout the entire tech development pipeline. I do think this is a new era."

There are many aspects to AI, from training data to AI architecture to collecting AI system outputs applying it. Each aspect is best suited for certain strategies to maximize patent protection. This presentation will provide in-depth information on patent and other IP trends for cutting edge AI technology. Points covered will include identifying AI technology suitable for patent protection, identifying whether and when to pursue patent protection for AI, and trends in patent protection for AI. Speaker Bio Steve Bachmann is the founder of Bachmann Law Group, specialized in patent and intellectual property matters, and has practiced IP law in Silicon Valley for 20 years.