Quantum Computing and Future of AI(Duke University, Professor, IonQ Inc., Co-Founder & CTO): Quantum computing is an entirely new framework of computing, and promises exponential speedup over conventional computers in some important computational tasks. This disruptive technology is still in its early days of development, and its economic impact remains unclear. Yet, the investment in this technology from nation states and private sector is on a steep rise around the world, and the pace of technology and market development is accelerating. Jungsang Kim has been at the forefront of this space race towards realizing practical quantum computers that has the potential to transform the landscape of many industries. In this session, he will discuss the potential impact of quantum computing, and discuss how a new market may develop in the coming years.
2021 Artificial intelligence outlook: Language, automation and trust are key to AI: IBM SVP
Yahoo Finance’s Adam Shapiro spoke with IBM Cloud and Data Senior Vice President Rob Thomas about the future of artificial intelligence in business.
The Rise of Supersoldiers – How AI Changes Everything
Artificial Intelligence is touching almost every aspect of our lives. It’s reasonable to expect AI influence will only increase in the future. One of many fields heavily influenced by AI is the military. Particularly in the development of Supersoldiers. The notion of super-soldiers enhanced with biotechnology and cybernetics was once only possible in the realm of science fiction. But it may not be too long before these concepts become a reality.
A new worldwide arms race is pitting countries against each other to be the first to successfully create real genetically modified super soldiers by using tools such as CRISPR. Understandably many of these human enhancement technologies raise health and safety questions and it is more likely these enhancements will first gain traction in countries that do not place as much weight on ethical concerns.
According to US Intelligence, China has conducted “human testing” on members of the People’s Liberation Army in hope of developing soldiers with “biologically enhanced capabilities.
This has made the U.S. military’s top intelligence agencies increasingly worried but the Pentagon has significantly invested in its own research in AI and in the extension of the human senses beyond their current physical limitations, to provide soldiers with superhuman abilities.
The basics of brain-machine interfaces with AI are being developed for the military, and if the results are as successful as scientists hope they will be, soldiers could one day be enhanced with cybernetics, effectively becoming trans-human soldiers.
The US Military is also examining newly scientific tools, like genetic engineering, brain chemistry, and shrinking robotics, for even more dramatic enhancements. But most of this advanced technology remains classified.
We’re still a long way from the kind of capabilities required for doomsday scenarios like super-soldiers or genetically-targeted biological weapons, but recent developments suggest there’s real danger of a new genetic arms race in the making.
Generative Adversarial Networks and the Matrix
This video hypothesizes that the Architect and Oracle characters of The Matrix are adversarial AI systems working together to improve the Matrix itself.
Is OpenAI’s GPT-3 Overhyped? | GPT-3, Six Months Later
GPT-3 is a large natural language machine-learning model.
What is GPT3? | GPT3 demo
GPT3 is a largest neural network every created by humanity (as of Dec 2020 atleast). It has 175 billion parameters, 4.6 million $ were spent in training it. It is a specific type of neural network called language model which can do different creative natural language processing tasks such as,
1) Write poems and essays, write news article
2) Convert english statement to an SQL query
3) Languge translation
4) Write code. (This sounds scary right?)
Can you tell the difference between a human voice and the voice of a machine?
Synthetic voices have become ubiquitous. They feed us directions in the morning, shepherd us through phone calls by day, and broadcast the news on smart speakers at night. And as the technology used to make them improves, these voices are becoming more and more human-sounding. This is the final frontier in synthetic speech: replicating not just what we say, but how we say it.
Rupal Patel heads a research group at Northeastern University that studies speech prosody—the changes in pitch, loudness and duration that we use to convey intent and emotion through voice. “Sometimes people think of it as the icing on the cake,” she explains. “You have the message, and now it’s how you modulate that message, but I really think it’s the scaffolding that gives meaning to the message itself.”
Patel says she grew interested in prosody after finding it was the only element of vocal communication that seemed to be available to people with some kinds of severe speech disorders. These patients were able to make expressive sounds, even if they could not speak clearly. In 2014, Patel founded a company to build custom synthetic voices for non-speaking individuals. VocaliD has since expanded to commercial brands and influencers.
Synthetic speech has come a long way over the years. At age nine, Siri is the oldest virtual assistant—but in the world of speaking machines, she’s a baby. People have been trying to synthesize speech since at least the 18th century, when an Austro-Hungarian inventor built a crude replica of the human vocal tract that could articulate entire phrases (albeit in a monotone).
Current machine-learning techniques can model human speech complete with awkward pauses and lip smacks. Still, training on thousands of samples per second is prohibitively expensive for most real-world systems; researchers, including those at VocaliD, are continually implementing newer and more efficient methods.
But even as the remaining gaps between human and synthetic speech are steadily closing, truly lifelike prosody continues to elude even the most sophisticated systems. Maybe what’s still missing requires machines to not only mimic humans, but also to feel like us.
Real, Live GPT-3 Apps You Can Use
Real, Live Apps using GPT-3 you can use, experiment with, and buy, check it out and have fun.
GPT-3 is over-hyped | George Hotz and Lex Fridman
GPT-3 has no memory. Yet, GPT has shown a surprising tendency to task generalization, with pretty good results on few-shot learning. It asks for at least some benefit of a doubt that a larger pre-trained transformer could continue get better at generalization.
Using GPT-3 to Write Technical Blog Posts
This shows you how SEOs could use GPT-3 to create technical blog posts. While the content generated isn’t fantastic, it could be a helpful addition to a writer’s toolbox.
