The ChatBot can't think, but produces much better responses than Google searches, and can be more entertaining than your WhatsApp chats or Instagram feed. This article tracks the recent development of generative AI tools in the context of work automation and asks whether ChatBots will take our jobs, where does that leave us humans and generates some interesting questions for society. In general, Artificial Intelligence, (AI) includes natural language processing, pattern recognition, and machine learning. We have all probably used AI in recent days with one of our apps, Netflix, Uber, Spotify, Prime. The way to look at AI is as a continuation of the general automation trend, more algorithms, and advanced statistics.

Picture yourself driving on a narrow road in the near future when suddenly another car emerges from a bend ahead. It is a self-driving car with no passengers inside. Will you push forth and assert your right of way, or give way to let it pass? At present, most of us behave kindly in such situations involving other humans. Will we show that same kindness towards autonomous vehicles?

Humans expect that AI is benevolent and trustworthy. A new study reveals that at the same time humans are unwilling to cooperate and compromise with machines. Picture yourself driving on a narrow road in the near future when suddenly another car emerges from a bend ahead. It is a self-driving car with no passengers inside. Will you push forth and assert your right of way, or give way to let it pass?

As it becomes easier for humans to do the mundane with the advent of artificial intelligence (AI), the ability of human kind to process complex emotions will become imperative. While the evolution of AI and machine learning--and how they will change our lifestyle, the markets and workforce in many sectors--has been staring in our face, it is important to know the following facts. AI and machine learning will quickly surpass human beings in many areas and this will shift the skillset required by any worker to stay relevant. Those who want to stay relevant will need to focus on skills and capabilities that machines have trouble replicating, that is, understanding and interacting with fellow humans. There are a lot of things that machines can do better than humans, including jobs, which until recently were seen as impossible without human intervention.

Here's a question: do you consider yourself to be a trusting person? Or let me put it another way: would you put your life in the hands of a total stranger? This morning I woke up. I switched on the light -- trusting that I wouldn't be electrocuted by a faulty lamp, or cord, or socket. I prepared my breakfast -- trusting that I wouldn't be poisoned by salmonella in my factory-processed muesli.

Here's a question: do you consider yourself to be a trusting person? Or let me put it another way: would you put your life in the hands of a total stranger? This morning I woke up. I switched on the light – trusting that I wouldn't be electrocuted by a faulty lamp, or cord, or socket. I prepared my breakfast – trusting that I wouldn't be poisoned by salmonella in my factory-processed muesli.

How we know each other - how we perceive and construct the identity of our fellow humans - is a difficult question, entangled in the subjectivity of our social perceptions and the many and often opaque motivations of those whom we are attempting to comprehend. It is a difficult question in the real world, where signals as subtle as the slightest raise of an eyebrow can indicate, to those astute enough to notice, a wealth of information about one's allegiances and beliefs - and where we exist amidst a cacophonous abundance of such signals. It is an even more difficult question in the virtual world, where the medium has nearly silenced the cacophony, leaving us to seek scarce hints of identity amidst the typed messages and static, stilted homepages. This chapter will address the problem of teleidentity: how do we - or do we - "know" another person whom we have encountered in a mediated environment? The epistemological ramifications of this question are several.

I was eager to learn about the latest developments in the Semantic Web through the lens of a "new kind of semantics" as Abraham Bernstein et al. explored in their Viewpoint "A New Look at the Semantic Web" (Sept. If I understand it correctly, semantics is a mapping function that leads from manifest expressions to elements in a given arbitrary domain. Based on set theory, logicians have developed a framework to set up such mapping for formal languages like mathematics, provided one can fix an interpretation function. On the other hand, 20th-century logicians (notably Alfred Tarski) warned of the limits of the framework when applied to human languages. Now, to the extent it embraces a set-theoretic semantics (as in the W3C's Ontology Web Language), the Semantic Web seems to be facing exactly such limitations or experiencing, dealing with, and suffering them.