Artificial Intelligence 1. Artificial Narrow Intelligence 2. Artificial General Intelligence 3. Artificial Super Intelligence 4. Computational Science Computational biology Computational Chemistry Computational PhysicsAll accelerated with Moore's Law AND autonomous AI programing worldwide 5. These three together will change… what we think is possible Artificial Intelligence Moore's Law Computational Science 6. Increasing Intelligence: both Individual and Collective Intelligence EU, US, China Human Brain Projects; Google & IBM artificial brain projects 7. Steve Jobs and Bill Gates 1991 By 2030 billions of people could be augmented geniuses, and what could they create? If Then Nano- technology Synthetic Biology Artificial Intelligence Robotics 3-D Printing Augmented Reality Nano- technology xxx Synthetic Biology xxx Artificial Intelligence xxx Robotics xxx 3-D Printing xxx Augmented Reality xxx Emerging Technologies Matrix 14. IoT AI Contact Lens – always in Virtual Reality connected to the word Hands-free, phone free, laptop free, AI-human symbiosis 15. Humans becoming cyborgs Conscious-Technology Age Built environment becoming intelligent When the distinction between these two mega trends becomes blurred, we will have reached the Post-Information Age 16. Simplification/Generalization of History and an Alternative Future Age / Element Product Power Wealth Place War Time Agricultural Extraction Food/Res Religion Land Earth/Res Location Cyclical Industrial Machine Nation-State Capital Factory Resources Linear Information Info/serv Corporation Access Office Perception Flexible Conscious- Technology Linkage Individual Being Motion Identity Invented 17. Consciousness Technology Technology Changes Consciousness and Consciousness Changes Technology 18. Future Mind: Artificial Intelligence by Jerome C. Glenn Merging the Mystical and the Technological in the 21st Century 1989 19.

Google's search algorithm appears to be systematically promoting information that is either false or slanted with an extreme rightwing bias on subjects as varied as climate change and homosexuality. Following a recent investigation by the Observer, which uncovered that Google's search engine prominently suggests neo-Nazi websites and antisemitic writing, the Guardian has uncovered a dozen additional examples of biased search results. Google's search algorithm and its autocomplete function prioritize websites that, for example, declare that climate change is a hoax, being gay is a sin, and the Sandy Hook mass shooting never happened. The increased scrutiny on the algorithms of Google – which removed antisemitic and sexist autocomplete phrases after the recent Observer investigation – comes at a time of tense debate surrounding the role of fake news in building support for conservative political leaders, particularly US President-elect Donald Trump. Facebook has faced significant backlash for its role in enabling widespread dissemination of misinformation, and data scientists and communication experts have argued that rightwing groups have found creative ways to manipulate social media trends and search algorithms.

Nintendo has launched Super Mario Run, its first game for iOS, and found itself worth $2 billion less than when it started. The game received huge amounts of hype for its combination of nostalgia and excitement, and as a signal that Nintendo might look to move more of its games off its less popular consoles. But it has already been hit by some backlash, over its high price and a mode that means it will only work if players have an internet connection. Those concerns appear to have dragged down Nintendo's share price, which fell by about 5 per cent in the wake of the release. That meant that the value of the company dropped by around $2 billion.

A Real World Exa... Watch a neural network describe what it sees on a stroll through Am... The Difference Between Junior, Mid-Level, And Senior Data Scientist... Data Storage on DNA Can Keep It Safe for Centuries Single Artificial Neuron Taught to Recognize Hundreds of Patterns 7 Reasons why the Algorithmic Business will Change Society Five Forces Pushing Statistics Expertise Out of Data Analysis Encryption Is Being Scapegoated To Mask The Failures Of Mass Survei... Encrypted Messaging Apps Face New Scrutiny Over Possible Role in Pa... Google Just Open Sourced the Artificial Intelligence Engine at the ... Latest Ford Focus electric creates 10 terabytes of data, per hour! Will NoSQL be the undoing of Oracle's database reign? Google's Inbox uses machine learning to speed up email replies Pluto gets a little psychedelic in this week's space photos - Nice image produced with principal components analysis Autism cases in U.S. jump to 1 in 45 - Example of bad analysis: there are more White people with autism than from other races, because there are more Whites than other races in US. When accounted for this fact, the conclusion must be reversed.

For a service provider, being able to anticipate its customer's behaviour has three major benefits. It can generate customer delight, prevent customer exhaustion, and improve the company's ROI. Let's look at each of these benefits through three different use cases in the Customer lifecycle: Complaints Management, Customer Upsell and Customer Retention. A dissatisfied customer, filing a complaint is difficult to manage. He is very often passionate about his claim - whether it is justified or not - and there is sometimes little which can be done to change his perception and his opinion towards the service he initially subscribed to. If however the company could tell precisely which customers are going to complain and when, it could avoid the management of a complaint by calling them pre-emptively to enquire about their satisfaction and offer them an incentive or a boon.

When I attend customer engagement and industry events, I inevitably field lots of questions that are close to the heart of a data scientist. Many executives are confused by the concepts of machine learning, deep learning, memory-based learning, and artificial intelligence. They wonder about the differences in these technologies, how everything fits together, and what they need to pay attention to. They wonder whether they need all of it or just some of it, and what they need to do to get started. And, yes, I hear people ask whether the ultimate goal is to replace humans with computers.

I'm a software engineer by training and I've had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my "in". That's why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. This time it's not a paper – it's the actual software they use internally after years and years of evolution.

Evernote has reversed proposed changes to its privacy policy that would allow employees to read user notes to help train machine learning algorithms. CEO Chris O'Neill said the company had "messed up, in no uncertain terms." The move by the note-taking app follows protests from users, some of whom have threatened to drop the service after the company announced that its policy would change to improve its machine learning capabilities by letting a select number of employees, who would assist with the training of the algorithms, view the private information of its users. The machine learning technologies would make users more productive as they would allow the automation of functions now done manually, like creating to-do lists or putting together travel itineraries, O'Neill had said earlier on Thursday in defense of the proposed changes. Evernote employees would only see random content in snippets to check that the features are working properly but they wouldn't know who it belongs to, and personal information would be masked, he added.

This paper proposed the fancy method which replaced the traditional RNNs with conv1d dilated and causal conv1d, and they achieved fast training and state-of-the-art performance on character-level translation. I've replaced the Sub Batch Normal with Layer Normalization for convenience. Latent dimension is 400 because Comtrans corpus in NLTK is small. I've replaced the Sub Batch Normal with Layer Normalization for convenience. Latent dimension is 400 because Comtrans corpus in NLTK is small.

"Artificial Intelligence" is all set to change our life as well as perspective. With digitization on an incredible rise, AI to have a dominating impact on our life. According to Ericsson Consumer Lab's global research activities of over more than 20 years, representing 27 million citizens as well as data from an online survey of advanced internet users in 14 major cities across the world, AI will become a lot smarter in 2017. It has also found that VR will be indistinguishable from physical reality in three years. Following are the ten ways AI will change the world in 2017.