Learn more about Robotic Process Automation here: http://www.nice.com/websites/rpa/
Automate repetitive tasks and set your people free to focus on what really counts – enhancing the
customer experience.
Share to set it free: https://youtu.be/tYyTEignHQA

Senior vice president of Alloy Digital and general manager of The Escapist magazine, the mouthpiece of the gaming generation.

http://www.binghamton.edu/
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In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

Do you think you’re good at spotting fake videos, where famous people say things they’ve never said in real life? See how they’re made in this astonishing talk and tech demo. Computer scientist Supasorn Suwajanakorn shows how, as a grad student, he used AI and 3D modeling to create photorealistic fake videos of people synced to audio. Learn more about both the ethical implications and the creative possibilities of this tech — and the steps being taken to fight against its misuse.

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Book review for

Architects of Intelligence: The truth about AI from the people building it
by: Martin Ford

*유튜브 한글 자막 제공
*Korean captions available

AI expert Stuart Russell argues that the field of AI will undergo a profound shift. Machines will need to understand human values, and in the process we will understand them better ourselves.

인공지능 권위자 스튜어트 러셀 교수는 앞으로 인공지능 분야가 겪게 될 근본적인 변화에 대해 이야기한다. 그는 기계가 인간의 가치를 배워야만 하며, 그 과정을 통해 우리 또한 인간의 가치에 대해 더 많은 것을 깨닫게 될 것이라고 주장한다.

For nearly half a century, artificial intelligence always seemed as if it just beyond reach, rarely more, and rarely less, than two decades away. Between Watson, Deep Blue, and Siri, there can be little doubt that progress in AI has been immense, yet “strong AI” in some ways still seems elusive. In this talk, I will give a cognitive scientist’s perspective on AI. What have we learned, and what are we still struggling with? Is there anything that programmers of AI can still learn from studying the science of human cognition?

Artificial intelligence may help in any diagnosis because it can scan millions of medical patterns and offer doctors possible causes of why you aren’t feeling well.

Watch more here: https://rethink.ft.com/

Theoretical neuroscientist and entrepreneur Vivienne Ming believes that AI and what she calls “augmented intelligence” mean we’ll eventually have people who are artificially smarter than others. But will we create super doctors and super teachers or use AI to simply replace them?

Human in the loop Machine learning and AI for the people

Paco Nathan is a unicorn. It’s a cliche, but gets the point across for someone who is equally versed in discussing AI with White House officials and Microsoft product managers, working on big data pipelines and organizing and part-taking in conferences such as Strata in his role as Director, Learning Group with O’Reilly Media.

Nathan has a mix of diverse background, hands-on involvement and broad vision that enables him to engage in all of those, having been active in AI, Data Science and Software Engineering for decades. The trigger for our discussion was his Human in the Loop (HITL) framework for machine learning (ML), presented in Strata EU.

Human in the loop

HITL is a mix and match approach that may help make ML both more efficient and approchable. Nathan calls HITL a design pattern, and it combines technical approaches as well as management aspects.

HITL combines two common ML variants, supervised and unsupervised learning. In supervised learning, curated (labeled) datasets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data. In unsupervised learning, the idea is that running lots of data through an algorithm will reveal some sort of structure.

The less common ML variant that HITL builds on is called semi-supervised, and an important special case of that is known as “active learning.” The idea is to take an ensemble of ML models, and let them “vote” on how to label each case of input data. When the models agree, their consensus gets used, typically as an automated approach.

When the models disagree or lack confidence, decision is delegated to human experts who handle the difficult edge cases. Choices made by experts are fed back to the system to iterate on training the ML models.

Nathan says active learning works well when you have have lots of inexpensive, unlabeled data — an abundance of data, where the cost of labeling itself is a major expense. This is a very common scenario for most organizations outside of the Big Tech circle, which is what makes it interesting.

But technology alone is not enough. What could be a realistic way to bring ML, AI, and automation to mid-market businesses?

AI for the people
In Nathan’s experience, most executives are struggling to grasp what the technology could do for them and identify suitable use cases. Especially for mid-market businesses, AI may seem like a far cry. But Nathan thinks they should start as soon as possible, and not look to outsource, for a number of reasons:

We are at a point where competition is heating up, and AI is key. Companies are happy to share code, but not data. The competition is going to be about data, who has the best data to use. If you’re still struggling to move data from one silo to another, it means you’re behind at least 2 or 3 years.

Better allocate resources now, because in 5 years there will already be the haves and have nots. The way most mid-market businesses get on board is by seeing, and sharing experiences with, early adopters in their industry. This gets them going, and they build confidence.

Getting your data management right is table stakes – you can’t talk about AI without this. Some people think they can just leapfrog to AI. I don’t think there will be a SaaS model for AI that does much beyond trivialize consumer use cases. “Alexa, book me a flight” is easy, but what about “Alexa, I want to learn about Kubernetes”? It will fall apart.

Artificial intelligence is being used to do many things from diagnosing cancer, stopping the deforestation of endangered rainforests, helping farmers in India with crop insurance, it help you find the Fyre Fest Documentary on Netflix (or Hulu), or it can even be used to help you save money on your energy bill.

But how could something so helpful be racist?

Become an Inevitable/Human: https://inevitablehuman.com/

Will artificial intelligence help people become better? Evgeny is asked this question every day.
Our dream is to teach the machine to cooperate with a moral person consciously and determine the place of a person in the future.

Scientist, entrepreneur, poet

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Laura Montoya is the Founder and Managing Partner of Accel Impact, including Accel.AI, a global Non-Profit Institute lowering the barriers to entry in engineering artificial intelligence, and LXAI (www.latinxinai.org) an initiative to create opportunity for Latinx in AI. She has been described as a natural and versatile leader with a passion for AI, Computer Science, Research, and Psychology. Her talk explores modeling AI algorithms for social impact through theory, applications, and correlations to real world experience. She describes concepts in reinforcement learning and deep learning, with analogies to evolution of the individual and societies.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx