andromedaangel

[andromedaangel]

Donald Trump launches Artificial Intelligence Initiative

A.I. has become a defining issue of our time, affecting national security, economic development, human rights, social media and many other domains. President Donald Trump signed an executive order launching the “American AI Initiative”, directing federal agencies to officially focus more on artificial intelligence technology.

The administration will be assigning federal agencies specific timelines for “deliverables” and expects to release more information about the initiative in the next six months.

The initiative is following at least 18 other countries that have announced their own artificial intelligence strategies.

The five “key pillars” of the American AI Initiative are:

Research and Development

Infrastructure

Governance

Workforce

International Engagement

The American AI Initiative follows several steps the Trump administration has already taken on A.I.

Early reaction is mixed. “The White House’s latest executive order correctly highlights AI as a major priority for US policymaking,” says Kate Crawford, a co-director of AI Now, a research institute at New York University in New York City. But she’s concerned about its focus on industry and apparent lack of input from academia and civic leaders. She says passing mentions of privacy and civil liberties don’t dispel worries about the Trump administration’s “troubling track record” on these issues.

“The White House’s latest executive order correctly highlights AI as a major priority for US policymaking, ... (but I am) concerned about its focus on industry and apparent lack of input from academia and civic leaders.”

— Kate Crawford (Co-Director of AI Now)

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[andromedaangel]

Five Ways CIOs are Deploying AI

Walmart uses 100s of bots to automate back-office processes.

Western Digital reduces CapEx by using artificial intelligence to optimise test equipment.

Bank of America and Harvard University’s Kennedy School collaborate on responsible AI development.

7-Eleven uses chatbots and research voice interfaces to innovate the user experience.

At Pearson artificial intelligence is at the heart of the latest product innovations.

Here are a few interesting videos about the AI reployments of these companies:

Walmart AI Bots

Bank of America Merrill Lynch

April 11th 2018, Catherine Bessant, chief operations and technology officer at Bank of America, discusses the bank's partnership with Harvard Kennedy School to establish the Council on the Responsible Use of Artificial Intelligence and how Bank of America uses AI. She speaks with Bloomberg's David Westin on "Bloomberg Daybreak: Americas”.

Pearson and AI in Education

Watch Pearson's Chief Technology and Operations Office, Albert Hitchcock, discusses AI in education on CNBCs Squawk Box. from 2019 Artificial Intelligence (AI) News http://bit.ly/2MJTagd via http://bit.ly/2mpXST1

[andromedaangel]

AI in Beer Brewing

Artificial intelligence (in this case machine learning based AI) is making its way into so many parts of our lives without us even realising it. One such case is, surprise, surprise, the brewing of beer.

There are now, in fact, breweries that are using A.I. to enhance beer production. This is both brilliant and unbelievable!

Using data to inform brewmasters’ decisions with the possibility of personalised brews makes AI-brewed beer definitely intriguing.

Queue IntelligentX

IntelligentX has the distinction of creating the world’s first beer utilising AI algorithms and machine learning to help adjust their beer recipes.

IntelligentX produces the world’s first beer brewed by artificial intelligence, which improves itself from customer feedback. We use a complex machine learning algorithm to determine what consumers like about our beers, then brew new versions which are more finely tuned to people’s tastes.

IntelligentX creates four different varieties of beer:

Black AI

Golden AI

Pale AI

Amber AI

IntelligentX asks customers to follow the URL link provided on the bottles/cans to give their feedback. By answering 10 questions customers 100k data points.

This data is processed by an AI algorithm, and then the brewer decides how to incorporate the algorithm's output(s). Rather than replacing the brewmaster, AI gives insights into how to make decisions based on customer feedback. In future it could be possible to order a beer based on a recipe customized to your personal preferences.

“The key question is: will AI be able to cure hangovers by making hangover-free beer?”

— Steve

Read the full article to find out more about Carlsberg’s involvement of AI in the beer-making process.

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[andromedaangel]

The State of Artificial Intelligence in 2019

The Verge published a great article about the state of AI in 2019. The ever increasing hype around AI is being used by virtually every industry and sector to add “freshness” to their product and services lineup.

They argue that never has the term AI meant so little, while it should be the most prominent thing we should consider for advances in 2019.

Siraj Raval also published a great video on the same topic:

The 10 predictions for 2019 in this video are:

Client-side training will become much more popular.

Kubernetes containers will become an integral part of the AI pipeline.

AutoML will become standard for supervised learning tasks.

Researches will focus on unsupervised & reinforcement learning.

Mainstream generative model usage.

Quantum machine learning in production.

More AI-specific hardware.

Data-driven decision making goes mainstream.

AI will create more jobs than it destroys.

“In the here and now, artificial intelligence — machine learning — is still something new that often goes unexplained or under-examined. ... In the future, it’ll be so normal you won’t even notice.”

— The Verge

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[andromedaangel]

Tesla Full Self-Driving Images Leaked

Tesla’s full self-driving autonomous vehicle feature is coming. An image leaked on Tuesday apparently shows the new user interface of a test-car. The image shows how the autopilot software/system views and interprets the world. The view has strong similarities to previous autonomous driving videos released by Tesla, there is now an option called "full self-driving” indicating a more advanced mode suggesting complete autonomy of the car.

Tesla engineering car leaked picture shows what Autopilot sees live with settings for ‘Full Self-Driving’ https://t.co/3qmia3XqHy by @fredericlambert pic.twitter.com/0X0OkPzNmr

— Electrek.Co (@ElectrekCo) 10 April 2018

The new feature builds on Tesla's current autopilot through software updates, but this is still speculative.

“... [we are] pretty excited about how much progress we’re making on the neural net front ... [it will] feel like, ‘well this is a lame driver, lame driver, well actually this is a pretty good driver, like holy cow this driver’s good.’”

— Elon Musk

Take a ride in a Tesla with Full Self-Driving Hardware.

It is unclear on when full autonomy will ship to the masses. Elon Musk said during a call in February that autonomy was around three to six months away, but he also said during a panel discussion at SXSW, last month that “self-driving will encompass all modes of driving by the end of the next year [2019].”

The autonomous future of cars is seemingly very nearly here.

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[andromedaangel]

How Companies use Machine Learning

Machine learning is headed for a major growth spurt. After ticking past the $1 billion mark in 2016, the machine learning market is expected to hit $40 billion by 2025, according to a new report by Research and Markets.

Where will all that growth come from? Everywhere!

Of course, the first challenge of machine learning is identifying a use case. Not sure where to start? To make the most of this explosive technology, consider how today's top companies, ranging in industry from retail to hardware to media, are using it:

IBM Watson inventor David Ferrucci discusses five ways machine learning will impact the world in the next ten years with Kellogg professor Brian Uzzi.

Target Retail giant Target discovered that machine learning can be used to predict not only purchase behaviour but also pregnancy. In fact, Target's model is so precise that it can reliably guess which trimester a pregnant woman is in based on what she's bought. After a father discovered through Target's persistent promotions that his 16-year-old daughter was pregnant, Target actually had to dial its initiative back by mixing in less specific ads. Most companies' promotions are driven by the seasons or holidays. Snow shovels go on sale in July, sunscreen in June. But consumers go through seasons in their own lives, too. The worst time to sell someone a car, for example, is right after she just bought one. It might be the best time, however, to market car insurance to that person. Machine learning can pick up on those rhythms, helping companies recommend their products to customers when the timing is just right.

Twitter When someone posts a photo on Twitter, she/he wants people to see it. But if the thumbnail is not right, nobody is going to click on it. Twitter seems to have solved this problem by using neural networks. In a scalable, cost-effective way, the social media firm is using machine learning to crop users' photos into compelling, low-resolution preview images. The result is fewer thumbnails of doorknobs and more of the funny signs just above them.

Apple Apple recently filed a patent that, in non-technical terms, implies that it's prioritizing cross-device personalization. In the near future, for example, a user's Apple Watch might suggest an iTunes playlist to match his heartbeat goal in another app.

Alibaba 500 million people shop with Chinese retail giant Alibaba. Each of those customers goes through a separate and distinct journey, from searching to buying. How does Alibaba track and tailor each of those 500 million journeys? With machine learning, of course. Alibaba's virtual storefronts are customized for each shopper. Search results turn up ideal products. Ali Xiaomi, a conversational chat-bot, handles most spoken and written customer service inquiries. Every element of Alibaba's business was built for the shopper engaging with it, and every action the shopper takes teaches the machine more about what the shopper wants.

Learn how to integrate Amazon Machine Learning with applications - Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data.

Machines can't learn everything about a business or its customers. But companies like Apple, Spotify and Alibaba are pushing that boundary back further and further. Now, with machine learning making disruptive innovation easier than ever before expect to see new startups disrupting the existing market leaders.

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[andromedaangel]

Is Machine Learning Right for You?

Are you considering using machine learning? First, you have to think about the problem you are trying to solve.

Speaking at the recent Code PaLOUsa conference in Louisville, Brian Korzynski, a senior application developer at United Shore, explained why it's essential that businesses understand the pros and cons of machine learning.

At Code PaLOUsa 2018, Brian Korzynski of United Shore explained why companies need to understand what machine learning truly is and what kind of problems it can solve before diving in.

“There’s been a lot of buzz in the news about what machining learning is and what it’s not, and there’s a lot of misconceptions that people get about what it really is. What it really is is a very specific set of algorithms that solve very specific problems. Machine learning is only targeted towards certain types of problems such as classifications, or regressions, recommendations, those kinds of things. The subset is a lot smaller than what a lot of people think, but a lot of it gets misconstrued, based upon a lot of the things you see in the news, such as like self-driving cars, and how machines are generating scripts for movies and things like that.”

— Brian Korzynski, United Shore

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[andromedaangel]

Tesla's New Autopilot Software is Massively Improved

We found a great video of Tesla's updated "autopilot" software which gives Tesla (founded by Elon Musk) electric cars (in this case a Model S) quite impressive self-driving capabilities. It is worth noting that this software is not supposed to be used without close oversight and control of the driver.

The road this test-drive happened on is the "Höhenstrasse" in the hills west of Vienna, a very scenic route, but likely a good challenge for the software as the cobblestone road is very windy (as in: it has a lot of turns - not wind #wink) and mostly has no road markings.

Elon Musk's company is doing a stellar job at developing and improving their cars; self driving vapabilities.

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[andromedaangel]

Amazon SageMaker AI Service

   Amazon revealed its SageMaker AI service today, allowing its customers to train machine learning models at massive scale while keeping costs down. Amazon uses novel techniques to keep the required compute power locked down while providing comparable performance.

When SageMaker takes in data to train a model, it uses a streaming algorithm that only makes one pass over the data that it gets fed. While other algorithms can see exponential increases in the amount of time and processing power needed, Amazon’s algorithms don’t. As data is streamed into the system, the algorithm adjusts its state — a persistent representation of the statistical patterns present in the information fed into SageMaker for training a particular system.

That state isn’t a trained machine learning model, though: It’s an abstraction of the data fed to SageMaker that can then be used to train a model. That provides a number of useful advantages, like making it easier for Amazon to distribute training of a model. SageMaker can compare the states of the same algorithms working on different data across multiple machines over the course of the training process, to make sure that all the systems are correctly sharing a representation of the data they’re being fed.

That same representation makes it easier to optimize the hyperparameters of a resulting machine learning model. Those parameters, which govern certain functions of the model, are key to creating the best machine learning system. Traditionally, data scientists would optimize those parameters by repeatedly training the same model with different parameters each time and picking the model that creates the most accurate final result.

However, that can be a time-consuming process, especially for models built using large amounts of data. With SageMaker, Amazon doesn’t have to do all the heavy lifting of retraining, since it can just use the streaming algorithm’s state.

All of this is in the service of creating a system that can handle incredibly large datasets running at global scale, something that’s important both for Amazon’s work on its own AI projects, as well as customers’ needs.

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[andromedaangel]

Google Releases Pixel 2 Portrait Mode Deep Learning Model

Many of Google’s machine learning efforts are open sourced so that developers can take advantage of the latest advancements. The latest release is for semantic image segmentation, or the technology behind the Pixel 2’s single lens portrait mode.

This deep learning model assigns semantic labels to every pixel in an image. In turn, categorization allows classifications like road, sky, person, or dog, and which part of a picture is the background and what is the foreground.

Applied to photography, the latter is leveraged on the Pixel 2’s Portrait Mode for shallow depth-of-field effects with only one physical lens. This use requires optimization especially in “pinpointing the outline of objects,” or being able to distinguish where a person ends and the background begins.

Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.

https://9to5google.com/2018/03/14/google-pixel-2-portrait-mode-open-source/

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[andromedaangel]

Stephen Hawking on Artificial Intelligence (✝ in Memoriam)

Today Stephen Hawking started his journey towards the nearest black hole and in memory of him and his prominent views on AI and it's future I collected these videos, facts, quotes and more for you.

Prof Stephen Hawking, one of Britain's pre-eminent scientists, has said that efforts to create thinking machines pose a threat to our very existence.

“The development of full artificial intelligence could spell the end of the human race.”

— Stephen Hawking

His warning came in response to a question about a revamp of the technology he uses to communicate, which involves a basic form of AI.

The theoretical physicist, who has the motor neurone disease amyotrophic lateral sclerosis (ALS), is using a new system developed by Intel to speak.

Machine learning experts from the British company Swiftkey were also involved in its creation. Their technology, already employed as a smartphone keyboard app, learns how the professor thinks and suggests the words he might want to use next.

Prof Hawking says the primitive forms of artificial intelligence developed so far have already proved very useful, but he fears the consequences of creating something that can match or surpass humans.

“It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”

— Stephen Hawking

In January 2015, Stephen Hawking, Elon Musk, and dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI. The letter affirmed that society can reap great potential benefits from artificial intelligence, but called for concrete research on how to prevent certain potential "pitfalls": artificial intelligence has the potential to eradicate disease and poverty, but researchers must not create something which cannot be controlled. The four-paragraph letter, titled "Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter", lays out detailed research priorities in an accompanying twelve-page document. Learn more ...

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[andromedaangel]

Using Amazon’s Mechanical Turk for Machine Learning Data

 Sometimes you already have a large amount of historical data and a precise ground truth knowledge about each data point, in which case your dataset is already labelled and all you need to do is clean, normalize, sub-sample, analyze, and train a model, and then iterate until you achieve a good evaluation.

But more often, all you have is a big bucket of raw unlabelled data and the process of manually building a consistent ground truth might be the most painful phase of your machine learning workflow. Some of these scenarios are well covered by companies and services that provide subject matter expertise about your specific context (linguistics, semantics, statistics, etc), usually at a very high cost. Other contexts, for example in the case of multimedia annotations, are way harder to handle, and it turns out that crowdsourcing might be a great way to cut down both costs and time.

What is Amazon Mechanical Turk?

Mechanical Turk – or MTurk – is a crowdsourcing marketplace where you (as a Requester) can publish and coordinate a wide set of Human Intelligence Tasks (HITs), such as classification, tagging, surveys, and transcriptions. Other users (as Workers) can choose your tasks and earn a small amount of money for each completed task.

How to build a model from Mechanical Turk results

Amazon Mechanical Turk will notify you when your results are ready and you will finally have a labelled dataset. In some cases, a few records might not have achieved any consensus, so could either improve your task instructions or, if the remaining dataset is big and statistically distributed enough to generate a useful model, simply discard them.

Conclusions

Amazon Mechanical Turk and other crowdsourcing platforms can be very useful in helping you to build your machine learning model from an unlabelled dataset.

Other solutions could involve unsupervised learning techniques, such as clustering and neural networks, which are pretty good at identifying patterns and structures in unlabelled data. However for most tasks, they are still far behind human intelligence. “Low-tech” solutions involving real humans will probably bring much higher accuracy, with an acceptable trade off between cost, complexity, and speed.

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[andromedaangel]

Time for Technology’s EQ to Equal its IQ

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The 2018 Tech Trends report released at SXSW 2018 predicts that by 2021 more than half of all computing in developed nations will be performed with voice.

“It’s basically time for technology’s EQ to equal its IQ,” Krettek said. “I think we have a lot of brute IQ, a lot of functional stuff that can be done, but this new paradigm is inherently social, and when you think of language, it’s like the marker of our species, it’s like we’re connecting with these things in a way where it can’t just be that functional cognitive layer. We’ve got to design for the emotional layer.”

“It’s like we’re going from the sharp edge to the rounded corner, going from blunt colors to gradients — like those are kind of the milky spaces I think will be really fascinating for us.”

Krettek shared her remarks Saturday at YouTube HQ at SXSW in Austin, Texas during a panel titled “The Power of Story Through the Lens of AI.”

Empathy from a machine isn’t actually possible, Krettek said, but her hope is assistants can someday achieve a kind of “empathic leap” where an assistant brings intelligence and a personality created by the Google Assistant team in a way that achieves a connection that feels more like a copilot at your side. It may not be human but it’s closer to you and more understanding than a sterile robot.

The SXSW panel took place days after Li published a New York Times op-ed about making AI that’s good for people, not just machines.

Frankfurter hamburger cupim boudin pancetta. Tenderloin ham hock turkey cupim, alcatra beef salami pig pancetta bacon sausage burgdoggen short ribs. Chuck jerky salami shoulder tenderloin, pancetta rump sirloin. Pork belly beef ribs ground round, turkey pork loin hamburger boudin capicola spare ribs rump ribeye pork. Alcatra filet mignon flank t-bone turkey, landjaeger tri-tip pork belly. T-bone flank ribeye burgdoggen tongue boudin. Turkey pastrami sirloin, turducken cow short loin alcatra rump doner drumstick leberkas fatback.

https://venturebeat.com/2018/03/11/google-empathy-lab-founder-ai-will-upend-storytelling-and-human-machine-interaction/

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[andromedaangel]

Pitfalls of Artificial Intelligence

Spicy jalapeno bacon ipsum dolor amet salami pancetta andouille porchetta prosciutto ribeye landjaeger shoulder brisket tri-tip ball tip boudin frankfurter tail turducken. Short ribs swine jowl leberkas doner turkey. Landjaeger frankfurter ham turkey andouille tongue jowl rump buffalo pork belly short ribs brisket corned beef flank. Filet mignon ball tip ribeye ham pork chop kevin kielbasa. Short ribs bresaola shoulder shankle. Corned beef tenderloin swine leberkas cow venison, meatloaf capicola sausage t-bone kevin picanha. Short loin andouille pancetta prosciutto kielbasa tongue beef.

No one could argue that life hasn’t been more convenient since the initial breakthrough of artificial intelligence (AI) in 2012. The world is moving faster and faster, driven by technology and innovation. From speech recognition to facial recognition allowing you to unlock the new iPhone with your face to cars self-navigating the streets in Silicon Valley, AI is all around us. And if a seating chart is telling for a tech company's priorities, Google’s AI researchers were recently moved to sit near the boss in its Silicon Valley office. However, with the hype and noise around AI, it’s easy for companies to take missteps along the way when trying to take advantage of the technology. As it’s always a good idea to exercise caution when deploying an AI-driven service, here are a few common pitfalls to watch out for:

1. Being Shielded by the Hype

Adopters need to look beyond the hype to accurately judge AI's benefits, as it’s far too easy for organizations to underestimate the time, knowledge, and data required to effectively implement AI systems.

2. Lacking IT Team to Manage AI Effectively

A major pitfall is lacking an IT team that has the expertise to effectively manage the AI system and interpret insights to their maximum value.

3. Trying to Keep Up With Google

Organizations also can’t fall into the trap of comparing themselves to Google. Developing and utilizing neural networks the correct way requires a ton of expertise and computing resources, something that Google clearly has.

4. Using AI to Answer All of an Organization’s Problems

It’s key to incorporate AI into facets of your organization, but utilize other key data center technologies alongside it.

Frankfurter hamburger cupim boudin pancetta. Tenderloin ham hock turkey cupim, alcatra beef salami pig pancetta bacon sausage burgdoggen short ribs. Chuck jerky salami shoulder tenderloin, pancetta rump sirloin. Pork belly beef ribs ground round, turkey pork loin hamburger boudin capicola spare ribs rump ribeye pork. Alcatra filet mignon flank t-bone turkey, landjaeger tri-tip pork belly. T-bone flank ribeye burgdoggen tongue boudin. Turkey pastrami sirloin, turducken cow short loin alcatra rump doner drumstick leberkas fatback.

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[andromedaangel]

Competitive Advantages of Deep Learning for Your Business

What do you think of when you hear about AI? Do you picture your favourite sci-fi movie or a book that you read when you were younger? In that favourite book or movie, were the robots smart? Could they learn?

Today’s software robots can. In AI, we can find a subset of machine learning called “deep learning,” which is defined as networks that can learn unsupervised from unstructured data.

Now the bigger question is: Are you ready to take advantage of deep learning in your business? The vast ocean of data grows exponentially every day. If you and your company can’t keep up, you’ll be left behind.

It’s time to utilize intelligent automation to help your business grow, keep organized, and stay on top of the competition. Let's discuss three advantages of deep learning for your company.

Cost and time benefits.

Quality and accurate results.

Job growth.

Utilizing deep learning for your business will save you money and time in more ways than one. You, your company, and your employees will benefit when you decide to take advantage of what AI has to offer. Will you make a move toward deep learning in your company?

Maybe they aren’t like the ones in books or movies, but robots are here, in one form or another. What is the future of AI? In some ways, that is up to you.

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[andromedaangel]

Is Artificial Intelligence a step forward or a step towards our imminent doom?

Brian Li (brian.li2@mail.mcgill.ca) at McGill University (Montreal, Quebec, Canada) sent me this article he (and a friend) wrote.

With the integration of this system in a human-like-machine, these “smart” robots are able to carry complex series of action and are becoming more and more predominant in our society.  This idea has generated concerns regarding jobs opportunities, where an average worker can be automated away by these new technological systems [1]. In contrast to an optimistic view of AI, many low-skill jobs will be replaced by sophisticated automated AI. With a rate of 37 percent, more and more Millennials with developed problem solving, and advanced technical skills have raised worries about the risk of their own redundancy which may be inevitable [2].

Research has shown that in the future decade, automation will take over 40 percent of jobs completely, automating the entire sector, however, it will also impact almost all occupations to an extent as well. In fact, 60 percent of all occupations could experience a 30 percent increase in automation with technologies existing today. If this alarming rate is possible with today’s technology, one can only imagine where the future might lead. One of the more automation susceptible jobs includes manual labour and operating of machinery: work is carried out in adaptable environments where small changes are increasingly simple to foresee. Such activities are prominently in sectors involving food service, accommodations, retail and manufacturing can see a 78 percent increase in automation in the next coming few years [3].

Moreover, the number of young people that do not possess a college degree is increasing and the world is experiencing an excessive number of workers that do not have the skills to secure a full-time employment [4]. The use of robots is not mitigating the problem of unemployment that societies are facing. In fact, it is estimated that “smart” robots will be eliminating 6 percent of all the existing jobs in the US by 2021. This is the moment to make rational decisions about this situation and to think carefully about the replacement of humans by machines. Andy Stern, the former president of the Service Employees International Union, commented [5]:

“Six percent is huge. In an economy that’s really not creating regular full-time jobs, the ability of people to easily find new employment is going to diminish. So we will have people wanting to work and struggling to find jobs because the same trends are beginning to occur in other historically richer job creation areas like banking, retail and healthcare.”

You would think the automation of jobs would be enough, but somehow, we have gone as far as to automate human consciousness as well. One of the current projects of David Hanson, “Sophia” the robot, involves the combination of human aspects such as cognitive, linguistic, gestural functions with AI technologies. Sophia can engage in discussions, recognize human emotions and emulate them herself [6]. This may seem like a huge step forward for the field of robotics, however, it is also a leap towards the replacement of humans themselves. This robot is described as a ‘social robot’ as it mimics the sentience of human beings, but what is the step forward from here?

We are now approaching a point where machines can improve themselves at a tremendous rate and AI can possibly replace humans. In fact, few high-profile voices such as Stephen Hawking and Bill Gates are explaining that technology can be harmful if not used appropriately. It is time to raise concerns about the possible consequences that these “smart” AI could cause societies [7].

References

Bryan, B. (2017) ALIBABA’S JACK MA: New Technology ‘may cause the Third World War’. Business Insider. Retrieved February 22, 2018, from http://www.businessinsider.com/alibaba-stock-price-jack-ma-artificial-intelligence-machine-learning-may-cause-world-war-iii-2017-6 

Marlin, D. (2018) Millennials, This is How Artificial Intelligence Will Impact Your Job For Better And Worse. Forbes. Retrieved February 22, 2018, from https://www.forbes.com/sites/danielmarlin/2018/01/16/millennials-this-is-how-artificial-intelligence-will-impact-your-job-for-better-and-worse/#7fd929a54533

Chui, M., Manyika, James. and Miremadi, Mehdi. (2016) Where machines could replace humans- and where they can’t (yet). McKinsey & Company. Retrieved February 22, 2018, from https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet

Dobbs, Richard., Madgavkar, A., Barton, D., Labaye, E., Manyika, J., Roxburgh, C., Lund, S. and Madhav, S. (2012) The world at work: Jobs, pay, and skills for 3.5 billion people. McKinsey & Company. Retrieved February 22, 2018, from https://www.mckinsey.com/global-themes/employment-and-growth/the-world-at-work

Solon, O. (2016) Robots will eliminate 6% of all US jobs by 2021, the report says. TheGuardian. Retrieved February 21, 2018, from https://www.theguardian.com/technology/2016/sep/13/artificial-intelligence-robots-threat-jobs-forrester-report

B. Goertzel, J. Mossbridge, E. Monroe, D. Hason, and G. Yu, “Loving AI: Humanoid Robots as Agents of Human Consciousness Expansion (summary of early research progress),” Cornell University Laval, 1-16

Nogrady, B. (2016) The real risk of artificial intelligence. BBC News. Retrieved February 22, 2018, from http://www.bbc.com/future/story/20161110-the-real-risks-of-artificial-intelligence

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[andromedaangel]

Xiaomi Heavily Investing in AI Research for Smartphone Chips

Xiaomi is researching chips that enable artificial intelligence (AI) on mobile devices, but has not made a decision on whether to make one.

"We are also working on doing a lot of research on AI on chips," - Wang Xiang of Xiaomi

There's been a lot of focus from smartphone makers to create chips that enable better AI applications. Huawei launched the Kirin 970 chip that is now in its high-end Mate 10 Pro device, Apple has put its own A11 bionic chip in the iPhone X, while Samsung has its Exynos series of silicon.

These specific AI chips allow more of the processes to happen on the device, rather than in the cloud. That means AI apps will be faster and smoother on the smartphone because its being processed on the device.

"The main purpose of doing our own chips is to learn the technology … deeper … we launched the first Surge-based smartphone … into the market. And we will continue to explore the technology and do the research. Not only the chipset, but also the AI and other related technologies," - Wang Xiang of Xiaomi

He added that the company has made no decision on whether to create its own AI chip.

https://www.cnbc.com/2018/02/26/xiaomi-doing-a-lot-of-research-on-a-i-chips-for-smartphones.html

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