I've read the book Life 3.0 by physicist & AI philosopher Max Tegmark, where he sets out a series of possible scenarios and outcomes for humankind sharing the planet with artificial intelligence. Tegmark immediately shoots down any notion that we are likely to be victims of a robot-powered genocide, and claims the idea we would programme or allow a machine to have the potential to hate humans is preposterous - fuelled by Hollywood's obsession with the apocalypse. Actually, we have the power, now, to ensure that if AIs goals are properly aligned with ours from the start, so that it wants what we want, then there can never be a'falling out' between species. In other words, if AI does pose a threat - and in some of his scenarios it does - it will not come from The Matrix's marauding AIs, enslaving humanity and claiming, like Agent Smith, 'Human beings are a disease. You are a plague and we are the cure'.
Artificial intelligence, that is AI, is one of the most phenomenal technologies that is being used in almost all industries. AI uses data stored within the system to predict outcomes and scenarios by thinking in different aspects like a human's brain, but in a very quick manner. In fact, AI systems provide much more efficient solutions to various problems as compared to those offered by human beings. This is the reason why AI is being immediately implemented by companies belonging to different sectors across the globe. School ERP software, which is one of the most recent technological developments in the education sector is no stranger to AI integration.
Nobody knows the future but we know death is future for everyone. What if the universal laws of birth and death does not apply? Singularity in Physics is a point where the known physical laws cease to apply. Like Gravitational laws break down beyond black hole making it an enigma. Vernor Vinge, science-fiction writer coined the term the Singularity in 1980s, in reference to hypothetical creation of superintelligent machines.
Artificial intelligence (AI) is rapidly improving, becoming an embedded feature of almost any type of software platform you can imagine, and serving as the foundation for countless types of digital assistants. It's used in everything from data analytics and pattern recognition to automation and speech replication. The potential of this technology has sparked imaginative minds for decades, inspiring science fiction authors, entrepreneurs, and everyone in between to speculate about what an AI-driven future could look like. But as we get nearer and nearer to a hypothetical technological singularity, there are some ethical concerns we need to keep in mind. Up first is the problem of unemployment.
There are several narratives that permeate around AI today. It has great promise, but it is not without pitfalls. Many early adopters have invested millions, but remain unimpressed and discouraged by the lack of returns thus far. With that said, and despite the challenges, the community has identified the problematic areas in the process, and a solution is emerging to getting through last mile AI challenges to scale to the enterprise: ModelOps. ModelOps tools manage the last mile delivery challenges associated with deploying, managing, and monitoring AI models into production systems.
Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.
Michael Littman is a computer scientist at Brown University. Please support this podcast by checking out our sponsors: – SimpliSafe: https://simplisafe.com/lex SUPPORT & CONNECT: – Check out the sponsors above, it's the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman On some podcast players you should be able to click the timestamp to jump to that time.
Over 9 out of 10 (93 per cent) UK employees believe that, by 2035, artificial intelligence (AI) technology investment will be the biggest driver of growth for their organisation. New research by Citrix, a software company, has investigated the different ways UK employees believe that AI will revolutionise the workplace by 2035. One of the ways in which UK employees see AI revolutionising the workplace is its impact on employee engagement. Over four out of five respondents (82 per cent) believed that AI would automate low value tasks which would ultimately improve employee engagement, freeing up employees' time so they could do'meaningful' work. Almost three-quarters of employees (72 per cent) also believe that AI will be critical for learning and development by 2035.
What will happen when robots become smarter than humans – will they want to kill us? His name is Ilya Sutskever and he believes that super intelligent machines won't hate us, but they will prioritise their own survival. Think about the way we treat animals. We're fond of them but we don't ask their permission to build a road; it'll be like that. His analogy is an extraordinary moment in this doom-laden documentary about the future of AI from Norwegian film-maker Tonje Hessen Schei – an eye-opening film if your anxiety levels are up to it. Another interviewee jokes that AI is being developed by a few companies and a handful of governments for three purposes – "killing, spying and brainwashing" and the film then briskly rattles through the worst-case scenarios facing human civilisation.
Jeremy Howard, AI expert and former President of Kaggle -- the world's largest data science community -- says this: "Python is not the future of Machine Learning. Here are the two major reasons why. Python, by default, is a very slow language. As Howard says, "unless you call out to some external code, you can't run anything in parallel." Indeed, speed comparisons between Julia and Python show that Julia is undoubtedly the winner, and it takes additional work to make Python faster, such as by using Cython, using speedup applications like PyPy or Numba, keeping the code as light as possible, avoiding loops, and more.