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Elon musk says we must merge with machines to compete with a.i

There is a long way from Elon Musk's vision of symbiosis between man and machine, which would require a much more granular understanding of the brain network that goes beyond the basics of motor control to more complex cognitive faculties like language and metaphor. 

Professor Panagiotis Artemiadis of Arizona State University has been trying to get more bandwidth using a 128-electrode EEG cap to allow a human to control a swarm of flying robots with their brain.

Humans won't become irrelevant until machines can replicate the human brain something Nicolelis believes is not possible.

Nicolelis argues that the brain contrary to what Musk and Singularity proponents like Ray Kurzweil say is not computable because human consciousness is the result of unpredictable, nonlinear interactions among billions of cells.

He agrees with Musk that if we can interface directly with machines we can produce a "quantum leap" in what digital infrastructure has produced today, but predicts that humans will retain ultimate control. 

Under these circumstances human skills diminish and people become subservient to machines. 

Better communication between humans and machines, particularly the transmission of emotional signals from humans, will be a powerful tool for building trust in automated systems, added Artemiadis.

"It's about making the machine more intuitive using brain signals to understand whether the human is distracted or tired."

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Ai learns to become 'highly aggressive'

Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained.

For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game.

These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.

Within DeepMind's work, the gathering game saw the systems trained using deep reinforcement learning to collect apples (represented by green pixels).

When a player, or in this case an AI, collected an apple, it was rewarded with a '1' and the apple disappeared from the game's map.

"Intuitively, a defecting policy in this game is one that is aggressive i.e., involving frequent attempts to tag rival players to remove them from the game," the researchers write in their paper.

For a deeper understanding check out Deepminds post: Understanding Agent Cooperation

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Edx: artificial intelligence (ai) free course

Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

What you'll learn:

- Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
- Building intelligent agents (search, games, logic, constraint satisfaction problems)
- Machine Learning algorithms
- Applications of AI (Natural Language Processing, Robotics/Vision)
- Solving real AI problems through programming with Python

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Udacity: artificial intelligence engineer: learn to build the impossible

Welcome to the Artificial Intelligence Nanodegree program, where virtually anyone on the planet can study to become an AI engineer! We’ve collaborated with leading innovators in the field to bring you world-class curriculum, expert instructors, and exclusive hiring opportunities. Topics include:

- Search and Optimization
- Logic, Reasoning & Planning
- Building Models of Probability
- Natural Language Processing
- Computer VisionAnd much more!

This Nanodegree program consists of two terms of three months each. Students must complete the full six months to earn their credential and graduate. Each term costs $800, paid at the beginning of each term. At this time, there are no scholarships or financial aid available.

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Jun 26 to 29, 2017: o’reilly artificial intelligence conference

The brightest minds in artificial intelligence will gather in New York to share how to implement AI in real world projects. Join us as we explore the future of AI—and how to get there from here.

Underneath all the hype, real breakthroughs in AI are happening, transforming how we do business. Intelligence engineers and developers are creating software that doesn't just do what it's told, but has the ability to anticipate the needs of its users and customers through a combination of pattern recognition, knowledge, planning, and reasoning.

The O’Reilly AI Conference brings the growing AI community together to explore some of the most promising applied deep learning and edge intelligence topics, including:

- Translating abstract AI into real numbers for business
- Deep learning models for computer vision solutions
- AI for structured business data
- How AI can power voice interfaces
- Natural language processing
- Tensorflow
- Using the cloud as an AI supercomputer
- Scaling and productizing conversational AI
- Affordable AI-capable products
- AI for digital advertising product recommendations
- Software and hardware breakthroughs for deep neural networks
- AI contributions in industry and engineering

If you're looking for groundbreaking research, compelling use cases, rock-solid technical skills, teardowns of successful AI projects—and maybe one or two truly crazy ideas that just might change the world—make plans to join us June 26–29 in New York for the O’Reilly AI Conference. Take a look at the schedule and start making your plans today. 

For more information and to see the agenda


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