Recent advancements in machine learning are reaching a level of sophistication that are exceeding the expectations of industry analysts and executives alike. We're familiar with Google DeepMind's AlphaGo that bested the greatest masters of the ancient Chinese game "Go" 10 years earlier than expected. More recently, a new exhibition at the New York Gallery Metro Pictures depicts machine-made images to people using algorithms. Retailers are redefining customer experiences with real-time personalization and convenience. Even most stock trades are governed by automated analysis of market outcomes and determination of future trends faster and more accurately than humans alone.

Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one's own. Theory of Mind is critical to effective communication and to teams demonstrating higher collective performance. To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, there has been much emphasis on research to make AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this work, we argue that for human-AI teams to be effective, humans must also develop a theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs, and quirks. We instantiate these ideas within the domain of Visual Question Answering (VQA). We find that using just a few examples (50), lay people can be trained to better predict responses and oncoming failures of a complex VQA model. We further evaluate the role existing explanation (or interpretability) modalities play in helping humans build ToAIM. Explainable AI has received considerable scientific and popular attention in recent times. Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.

The US government's mighty DARPA last year kicked off a research project designed to make systems controlled by artificial intelligence more accountable to their human users. The Defense Advanced Research Projects Agency, to use this $2.97bn agency its full name, is the Department of Defense's body responsible for emerging technology for use by the US armed forces. Significantly, it was DARPA's early funding of packet-switching network the Advanced Research Projects Agency Network (ARPANET) more than 40 years ago that helped bring about the internet. Coming bang up to date, the issue at the heart of the Explainable Artificial Intelligence (XAI) programme is that AI is starting to extend into many areas of everyday life yet the internal workings of such systems are often opaque, and could be concealing flaws in their decision-making processes. The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs).

The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of main concern the need to clarify the URIs' ontological meaning. The aim of this study is to investigate the relation between the ontological substance of KOS concepts and concepts revealed through the grammatical and syntactic formalisms of natural language. For this purpose, we examined the dividableness of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a classification scheme) by applying Natural Language Processing (NLP) techniques (i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF literals) of SKOS concepts. The results of the comparative analysis reveal that, despite the use of multi-word units, thesauri tend to represent concepts in a way that can hardly be further divided conceptually, while Subject Headings and Classification Schemes - to a certain extent - comprise terms that can be decomposed into more conceptual constituents. Consequently, SKOS concepts deriving from thesauri are more likely to represent atomic conceptual units and thus be more appropriate tools for inference and reasoning. Since identifiers represent the meaning of a concept, complex concepts are neither the most appropriate nor the most efficient way of modelling a KOS for the Semantic Web.

Chief financial officer (CFO) at global recruitment company Airswift, Tim Briant says artificial intelligence is going to disrupt finance departments completely, with bots replacing people. He has already witnessed how smart, automated systems can impact an industry. Mr Briant was previously CFO at recruitment firm Adecco, where many processes are automated, while some don't "have a human touch at all" and are simply processed by software. There's less impact at the higher end, but that this could change over time, he says. "When you are placing a six-figure engineer, there's an element of human contact and reassurance that people want," says Mr Briant.

When Gang Xu, a 46-year-old Beijing resident, needs to communicate with his Canadian tenant about rent payments or electricity bills, he opens an app called iFlytek Input in his smartphone and taps an icon that looks like a microphone, and then begins talking. The software turns his Chinese verbal messages into English text messages, and sends them to the Canadian tenant. In China, over 500 million people use iFlytek Input to overcome obstacles in communication such as the one Xu faces. Some also use it to send text messages through voice commands while driving, or to communicate with a speaker of another Chinese dialect. The app was developed by iFlytek, a Chinese AI company that applies deep learning in a range of fields such as speech recognition, natural-language processing, machine translation, and data mining (see "50 Smartest Companies 2017").

The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.

Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.

These days, it's tough to avoid newspaper headlines warning that artificial intelligence is coming for your job. The problem is that, often, the only thing these oversimplifications get right is that there is in fact an important connection between automation and work. What's surprising is how many examples there are of AI acting as the catalyst for new hiring, higher wages, and happier employees. The reality is that the impact of AI on the workforce is complex, nuanced, and still very much in transition. A Deloitte study of automation in the U.K. found that 800,000 low-skilled jobs were eliminated as the result of AI and other automation technologies.

Whether a business requires linear, departmentalized chatbot support for basic user interactions, such as customer service or tech support, or more complex AI bots equipped with natural language processing capabilities, bots will boost your business. From improved key performance indicators like customer conversion to better results in user-behavior-based metrics such as engagement rates, both chatbots and AI bots provide a foundation for sustainable business growth through improved user experiences, scalability, and low overhead, high return efficiency. As a leader in innovative marketing technologies, I have been involved in the enterprise application of bots to assist in various business objectives. I'm also a keynote speaker on bot technology and the impact bots can have on a multitude of revenue streams. Regardless of whether a business uses focalized chatbot technology or more advanced AI bots, businesses can advance the user experience with bots while managing expenses.