This paper proposes to revisit the Turing test through the concept of normality. Its core argument is that the Turing test is a test of normal intelligence as assessed by a normal judge. First, in the sense that the Turing test targets normal/average rather than exceptional human intelligence, so that successfully passing the test requires machines to "make mistakes" and display imperfect behavior just like normal/average humans. Second, in the sense that the Turing test is a statistical test where judgments of intelligence are never carried out by a single "average" judge (understood as non-expert) but always by a full jury. As such, the notion of "average human interrogator" that Turing talks about in his original paper should be understood primarily as referring to a mathematical abstraction made of the normalized aggregate of individual judgments of multiple judges. Its conclusions are twofold. First, it argues that large language models such as ChatGPT are unlikely to pass the Turing test as those models precisely target exceptional rather than normal/average human intelligence. As such, they constitute models of what it proposes to call artificial smartness rather than artificial intelligence, insofar as they deviate from the original goal of Turing for the modeling of artificial minds. Second, it argues that the objectivization of normal human behavior in the Turing test fails due to the game configuration of the test which ends up objectivizing normative ideals of normal behavior rather than normal behavior per se.

If your vehicle were self-driving on the road, will it crash into a truck towing a trailer as Tesla did in March 2019?a Despite the fatal accidents involving autonomous vehicles, such vehicles represent an unstoppable trend that will reshape the world. In this Viewpoint, we highlight why current autonomous vehicles would not be preferred by their users. Furthermore, we present a concise framework for profiling the characteristics of various autonomous vehicles based on intelligence quotient (IQ), ethical quotient (EQ), and adversity quotient (AQ). As presented in Figure 1, there are already major players focused on the automated driving market.

Every day, Every Hour, Every Minute, we are saying few simple words like "Hey Google" or "Hey Alexa" or "Hey Siri" to know something or to get our works done. "Hey Google, How do you work?" or "Hey Alexa, Why are you so smart?" or "Hey Siri, What's behind your success?" It's simply because as a client we never bother about these things. In any application clients are the ones who needs to be satisfied and in today's world these assistants are so much smarter that there is no reason to think or ask these things as a client. But as a Tech Enthusiast, or as a guy from CS background it's always too much fascinating to know about behind the scene technologies, these popular companies are using to make these smart assistants capable of extraordinary performance.

In 1950, 746 Million people lived in cities but just one hundred years later in 2050, this is anticipated to surpass 6 Billion – some 66% of the world's population. In this increasingly urban, data-driven and hybrid world that integrates the physical and the virtual, the concept of'smartness' comes to the fore. The vision of a'smart city' has been in existence for many years but it is only recently that advances in technology have enabled tangible progress towards its real-world actualization. I believe this is also critical to the successful implementation of the United Nation's 2030 Agenda for Sustainable Development (SDGS). So, what does a smart city mean to you?

The organisation of tomorrow will be built around data. This will require smart algorithms to make sense of all that data. AI will enable organisations to leverage data and embed smartness in every process and customer touchpoint. When you put smartness to work, it will empower your employees and customers and make your organisation more humane. When building the organisation of tomorrow, the first step is to datafy your organisation by collecting data at every process and customer touch point.

Our built environment is becoming one big computer. "Smartness" is coming to saturate our stores, workplaces, homes, cities. As we go about our daily lives, data is made, stored, analyzed and used to make algorithmic inferences about us that in turn structure our experience of the world. Computation encircles us as a layer, dense and interconnected. If our parents and our grandparents lived with computers, we live inside of them.

A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence. This was the home of Watson, the electronic genius that conquered Jeopardy! in 2011. The original Watson is still here--it's about the size of a bedroom, with 10 upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines' backs. It is surprisingly warm inside, as if the cluster were alive. Today's Watson is very different. It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred "instances" of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand.

Robot mean any machine made by man that can perform any action normally a human can performed. The history of man-made automated machines has much lengthier past. Greeks made simple automated machines use as tools and toys. Often one question arises in our minds why we use robots and What are the Benefits of Robots? The answer of this question is that the robots are cheaper than humans.

The organisation of tomorrow will be built around data. This will require smart algorithms to make sense of all that data. AI will enable organisations to leverage data and embed smartness in every process and customer touchpoint. When you put smartness to work, it will empower your employees and customers and make your organisation more humane. When building the organisation of tomorrow, the first step is to datafy your organisation by collecting data at every process and customer touch point.

I've heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. That's the most common question I get whenever I give a talk about AI. The questioners are earnest; their worry stems in part from some experts who are asking themselves the same thing. These folks are some of the smartest people alive today, such as Stephen Hawking, Elon Musk, Max Tegmark, Sam Harris, and Bill Gates, and they believe this scenario very likely could be true. Recently at a conference convened to discuss these AI issues, a panel of nine of the most informed gurus on AI all agreed this superhuman intelligence was inevitable and not far away. Sign up to get Backchannel's weekly newsletter. Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence. These claims might be true in the future, but there is no evidence to date to support them. In contradistinction to this orthodoxy, I find the following five heresies to have more evidence to support them. If the expectation of a superhuman AI takeover is built on five key assumptions that have no basis in evidence, then this idea is more akin to a religious belief -- a myth. In the following paragraphs I expand my evidence for each of these five counter-assumptions, and make the case that, indeed, a superhuman AI is a kind of myth.