Many meetings require creating a meeting summary to keep everyone up to date. Creating minutes of sufficient quality is however very cognitively demanding. Although we currently possess capable models for both audio speech recognition (ASR) and summarization, their fully automatic use is still problematic. ASR models frequently commit errors when transcribing named entities while the summarization models tend to hallucinate and misinterpret the transcript. We propose a novel tool -- Minuteman -- to enable efficient semi-automatic meeting minuting. The tool provides a live transcript and a live meeting summary to the users, who can edit them in a collaborative manner, enabling correction of ASR errors and imperfect summary points in real time. The resulting application eases the cognitive load of the notetakers and allows them to easily catch up if they missed a part of the meeting due to absence or a lack of focus. We conduct several tests of the application in varied settings, exploring the worthiness of the concept and the possible user strategies.

Although two different entities, humans and machines have coexisted for many centuries now. Binary, the language that enables machines to compute and process information, is a system of two elements. It is the basis of all the coding and forms the foundation of how machines interact with the world. How does, then, the wide spectrum of human thinking and imagination accommodate the co-existence of binary machines? When a machine is given a task, it is translated into a series of binary bits, ones and zeros, a language that it comprehends and uses to complete the assigned job.

There will always be interactions between machines and humans. When the machine has a high level of autonomy and the human-machine relationship is close, there will be underpinning, implicit assumptions about behavior and mutual trust. The performance of the Human-Machine team will be maximized when a partnership is formed that is based on providing mutual benefits. Designing systems that include human-machine partnerships requires an understanding of the rationale of any such relationship, the balance of control, and the nature of autonomy. Essential first steps are to understand the nature of human-machine cooperation, to understand synergy, interdependence, and discord within such systems, and to understand the meaning and nature of "collective intelligence."

Earlier, the importance of AI suffered from a widespread lack of familiarity, but the technology has been relentlessly transforming every walk of life. Its wide-ranging tools have stepped beyond global businesses and industries and created world-altering solutions that answer several critical questions for society, economy, and governance. Meanwhile, the advancements of robotics and integration with AI are helping to automate almost every industrial process to promote efficiency. Robots have gained newfound effectiveness with AI tools and systems. Recently, researchers have developed new computer vision systems that enable the integrated AI algorithms in robots to interpret scenes more accurately than other traditional systems do.

Digital Humanities is an emerging research area. Wikipedia says that "Digital Humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities." This statement doesn't fully reveal anything clear or concrete. Whatever the definition, it is important to develop more services where humans and machines work together better. In most cases, AI is not an independent machine that handles all tasks but a tool to help people. That's why we need research and development to get this interaction working better.

I recently started to follow an exciting and mind-bending philosophy online course at MIT called Minds and Machines. The course is a thorough, rigorous 12 Weeks Learning Path introduction to contemporary philosophy of mind, exploring consciousness, reality, artificial intelligence (AI), and more. It is definitively one of the most in-depth philosophy courses available online that I ever frequented. The first effect of starting study philosophy at Massachusetts Institute of Technology is that I'm asking more challenging questions… the second effect is that I'm writing more about those questions. I'm in this moment, exploring the relationship between the mind and the body, the capacity of computers to think, the way we perceive reality, and the perspective of the existence of a science of consciousness. As a first result, I've started to pay particular attention to one specific question that definitively has a lot to relate to my daily work as an AI expert: what is intelligence?

The screen lights up with a new text message from Scott, a professor at the University of Texas: How many legs does a camel have? His smiley face seems to suggest that like other 13-year-olds, he's playful and maybe just a little bit childish. But in any case, it doesn't seem like his response is a serious attempt to answer the question. All business, Scott pointedly ignores the light-hearted response: How many legs does a millipede have? Again, Eugene doesn't give an accurate answer and seems to get a little distracted: Just two, but Chernobyl mutants may have up to five. I know you are supposed to trick me.

The late theoretical physicist Albert Einstein has been brought back to life with a digital human platform that recreated the famous scientist's look and voice. Digital Einstein was developed to'put a friendly and well-known face on digital human technology' face between machines and humans.' Complete with the German accent, the digital copy speaks in a soft, friendly tone and is programmed with the same dry sense of humor as the real Einstein was said to have. Users can participate in daily quizzes and ask the AI-powered character questions about science, his life and work. Digital Einstein was developed to'put a friendly and well-known face on digital human technology' face between machines and humans' Einstein is well-known for his work in physics, specifically for the theory of relativity that changed the understanding of space time, gravity and the universe.

AI is mainly based on machine learning algorithms that learn from data – the underlying approach is data science. With this in mind, let's start with a crash course on data science. It is a truth universally acknowledged that company performance depends on several factors, and that many of those factors are variable. If life is prone to inconsistency, so is business. Much of this is because of the unpredictability of human behavior, which is why it is interesting to explore alternative approaches to grasp them.

If you're a business executive (rather than a data scientist or machine learning expert), you've probably been exposed to the mainstream media coverage of artificial intelligence or AI. You've seen articles in The Economist and Vanity Fair, you've seen emotional stories about Tesla Autopilot and the threat of AI to mankind by such luminaries as Stephen Hawking, and you might even have seen Dilbert make jokes about Artificial Intelligence and Human Intelligence. So if you're an executive who cares about growing your business, all this AI media coverage may prompt two nagging questions. First, is the business potential of AI real or not? Second, how does AI apply to my business?