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Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. AI investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies. 
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The AI Race - Documentary ABC TV The world of the future and how jobs will be impacted with artificial intelligence
Farid Mheir's insight:
WHY IT MATTERS The 30 min video provides a good overview of the potential impact of AI in certain industries. A great example comes from the field of law, where law assistants may soon be replaced (enhanced?) by AI tools such as Ailira http://www.ailira.com/
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The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems.
Farid Mheir's insight:
WHY IT MATTERS No surprise in this list but the hype cycle is always a great tool to visualize and spark strategy discussions around emerging technologies.
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A small company called Neodriven, led by former Tesla employee Matt Schulwitz, is announcing a product of the same name that goes on sale today for $1,495. It’s a modified version of the Comma Neo plans that Hotz open sourced, but if Schulwitz has his way, it will be an early player in that revolution.
Farid Mheir's insight:
WHY THIS IS IMPORTANT You can now buy hardware to turn your car into a self driving car. Makes me think of the Homebrew project where Jobs and Wozniak created the first Apple computer and started a revolution. Guys at comma.ai and neodriven.com are doing the same and it seems that the technology is there to enable this revolution: 3G/LTE, GPS, mobile phone, universal car interface, AI and image recognition. Interesting years ahead.
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An exclusive look at how Alphabet understands its most ambitious artificial intelligence project
Farid Mheir's insight:
WHY THIS IS IMPORTANT An in depth article about the Castle, Google self-driving car testing facility in a remote area of California. Full of interesting information about what it takes to get a car to drive with confidence on world scenarios.
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With simulation, we can turn a single real-world encounter — such as a flashing yellow left turn — into thousands of opportunities to practice and master a skill. Here’s how it works.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Google self driving cars rely on human drivers to drive millions of miles and learn the basic driving skills. This story explains how Google has built a self-driving car simulation software called Carcraft to go one step further and use real-world roads into millions of different driving scenarios. What is important here is the fact that a machine - here the simulator - can learn from mistakes and improbable driving scenarios and then *instantly* share that knowledge with *all* of the self driving cars in Google's fleet. Humans cannot do that. And this means computers will learn skills - driving, medecine, accounting, law, etc. - extremely fast in the coming years. Be ready to see self driving cars on the road much sooner than you expect.
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Artificial Intelligence seems to make headlines with every innovation, garnering both praise and fear. But is it really at the point where we should fear everyday AI? Here are 17 common place applications for AI systems and a look into where the technology could be going.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Feels like AI is everywhere these days. True. This list may help you draw the same conclusion I have: look at any task that requires analysis of large volume of data or visual images or sound and there is a good chance AI will be able to help improve them.
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Meet Todai Robot, an AI project that performed in the top 20 percent of students on the entrance exam for the University of Tokyo -- without actually understanding a thing. While it's not matriculating anytime soon, Todai Robot's success raises alarming questions for the future of human education. How can we help kids excel at the things that humans will always do better than AI?
Farid Mheir's insight:
WHY THIS IS IMPORTANT The video is short and provides very good explanation of how the AI can pass the University entrance exam without understanding a thing. Nevertheless, I find this amazing because once AI learns something, or acquires a skill, *every* other AI can benefit from it. As opposed to humans who *individually* have to learn new things for themselves. Thus, we can expect AI to learn at an exponential rate and become truly "intelligent" very rapidly. This example should be a wake up call for all of us.
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Bitmain will probably make around $200M-$250M in profits this year just from their BM1387 chip (they have other products, including a Scrypt ASIC). Even after including ongoing R&D and other future investment, they are likely worth well over $1 Billion as a business and are probably the largest and most profitable company in Bitcoin.
Farid Mheir's insight:
WHY THIS IS IMPORTANT With bitcoin value at 4K$ USD, companies that specialize in dedicated hardware to mine bitcoins have become more profitable than ever. I find it interesting that this one, bitmain, which appear to be the leader, is Chinese. Morever, one should notice that mining bitcoin is very similar to cracking password or encryption, but it seems that these devices cannot be used for that purpose (https://security.stackexchange.com/questions/62800/is-it-possible-to-use-bitcoin-miners-to-decrypt-files-communication ) . however, the expertise can easily be refocussed on password cracking of encryption.
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In October 2016, in a big milestone for artificial intelligence, Microsoft unveiled a system that can transcribe the contents of a phone call as well or better than human professionals. But while Microsoft's system had fewer transcription errors than the average human transcriptionist, it still couldn't best a team of trained humans. So, the world of academia fired back with a new challenge: Lower the error rate to below what human teams can do. Now Microsoft has done just that.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Speech recognition is most important for business tasks and new computer interfaces that are small and pervasive (cannot fit a keyboard and mouse on a watch). This new research shows that it is possible to get 5.1% error rates on transcribing phone conversations. This is great but not yet error-free : expect more research to improve this extremely fast though.. This ushers in an era where your cell phone will record everything you say in a meeting - or elsewhere - and transcribe it for you to search the text for reference or recall the actual audio to show proof. Imagine how useful that can be when listening to a conference or class and take notes for you! Direct links to the Microsoft blog post and report are here: report: https://www.microsoft.com/en-us/research/wp-content/uploads/2017/08/ms_swbd17-2.pdf
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In the new study, the neural net learned what mouth shapes were linked to various sounds. The researchers took audio clips and dubbed them over the original sound files of a video. They next took mouth shapes that matched the new audio clips and grafted and blended them onto the video. Essentially, the researchers synthesized videos where Obama lip-synched words he said up to decades beforehand.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Fake news is difficult to debunk because we are trained to believe what we read has been vetted and checked for validity. Images and videos are even worse because, until now, it was difficult to alter them in any meaningful way. Technologies like this may change that forever. Watch out for elections 202 in the USA.
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Relying on an army of amateurs might seem odd, but it remains the most efficient way of training AI. “It’s pretty much the only way,” says Premkumar Natarajan, who specializes in computer vision at the USC Information Sciences Institute.
Farid Mheir's insight:
WHY THIS IS IMPORTANT AI has huge potential but it appears that for now humans must teach machines. This article and the attached video both show what it takes to crowdsource that learning from thousand of individuals and their computer. Basically using the most basic human functions - vision and object recognition - to tell computers what they should see on an image.
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Developers have built more than 15,000 skills with the Alexa Skills Kit. Explore the stories behind some of these innovations, then start building your own skill. If you’re serious about getting into building voice UIs, we’d like to help you explore.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Alexa is a nice little gadget that uses artificial intelligence to understand language and answer questions. It has a vocabulary and skills but what may set it apart is the ability for anybody to develop new skills for the device. Any developer can add features to the devices which means that it may learn at an exponential rate. 15000 skills today is impressive but not yet amazing - although most skills are gadget. But think back to the iPhone 10 years ago and its few apps. Now consider the millions of apps on the Apple store today and you may also agree with me that this is important...
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Researchers from the Facebook Artificial Intelligence Research lab (FAIR) recently made an unexpected discovery while trying to improve chatbots. The bots — known as “dialog agents” — were creating their own language — well, kinda. Using machine learning algorithms, dialog agents were left to converse freely in an attempt to strengthen their conversational skills.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Humans may not be so smart after all if it takes a few simple neural nets to start to create new languages and communicate. The article is just a teaser, if you want to know more you have to go the the Facebook research group blog to read the full thing. https://research.fb.com/category/facebook-ai-research-fair/ https://code.facebook.com/posts/1686672014972296/deal-or-no-deal-training-ai-bots-to-negotiate/ https://code.facebook.com/posts/266433647155520/parlai-a-new-software-platform-for-dialog-research/
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ARK Invest aims to identify large-scale investment opportunities resulting from technological change. We believe innovation is key to growth. From a broad spectrum of disruptive innovations, Big Ideas represents our annual breakout of technologies that we believe will accelerate significantly in the months ahead.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Investment firms tend to be very traditional in their approach. This one appears to be embracing the digital transformation and making very insightful research that - from what they claim - guides their investments. Great idea. Now we'll see if their assumptions prove right and generate more returns than other investment guidance. I have a feeling it will.
Topics covered: 3D printing, additive manufacturing, artificial intelligence, automation, autonomous taxis, autonomous vehicles, bitcoin, blockchain, CRISPR, cryptocurrency, deep learning, dna sequencing, fintech, genomics, machine learning, mobile, robotics, shared autonomous vehicles
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Interest in auto tech has risen dramatically in recent years. Technology has always been a staple of automotive research and development, but the past few years have seen a technology arms race as advances in IoT and artificial intelligence disciplines filter down to the automotive sector. Startup investment has risen dramatically as concepts once reserved for science fiction (like autonomous vehicles) move towards commercialization. We analyzed the explosion of activity in the auto tech space using the CB Insights platform in our full webinar.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Automobile tech combines so many of the high tech solutions and technologies - from AI to new batteries - that it makes for a great case study to understand how much transportation will change in the coming years. And with it, the possibility of business transformation from eCommerce delivery to supply chain improvements to employee getting to work with little stress and no traffic jams.
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From cloud computing and lithium ion batteries to 3D printing and containerization, we collected the technologies that have done the most to shape the internet era.
Farid Mheir's insight:
WHY THIS IS IMPORTANT I usually repost those things around new years, with all the predictions etc. but this list seemed to me so appropriate and so true that I wanted to share now.
Tumbling Stones's curator insight,
May 23, 2017 9:31 AM
WHY THIS IS IMPORTANT I usually repost those things around new years, with all the predictions etc. but this list seemed to me so appropriate and so true that I wanted to share now.
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From
medium
Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years?
Farid Mheir's insight:
WHY THIS IS IMPORTANT More research = more discoveries so we can expect even more practical uses of machine intelligence in the coming years...
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Thanks to machine learning, it’s becoming easy to generate realistic video, and to impersonate someone.
Farid Mheir's insight:
WHY THIS IS IMPORTANT If you thought fake news was bad, well an era of fake audio and news clips is about to dawn on us. We need regulations to govern this otherwise there will be consequences in the political, business and personal fronts for sure...
Also see this other blog post on the subject, as it related to fake actors in movies, a much less troublesome use of the technology: http://sco.lt/8Eglqj
This field is moving fast if you compare this to a blog post of January 2016: http://sco.lt/85op5l
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Systems able to recognize sounds familiar to human listeners have a wide range of applications, from adding sound effect information to automatic video captions, to potentially allowing you to search videos for specific audio events. Building Deep Learning systems to do this relies heavily on both a large quantity of computing (often from highly parallel GPUs), and also – and perhaps more importantly – on significant amounts of accurately-labeled training data. However, research in environmental sound recognition is limited by currently available public datasets.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Training data is crucial for machine intelligence. This kind of stuff will help researchers improve their AI solutions. What is most important is the data has been crowdsourced from youTube videos. It used to be we were the product, not it seems we are the data...
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Farid Mheir's insight:
WHY THIS IS IMPORTANT Based on a detailed report, this article provides a chockful of stats about investments in certain technologies. Although based on a survey, it confirms certain trends that are widely accepted - so there should be no surprises for readers of this blog.
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“Let us consider an augmented architect at work. He sits at a working station that has a visual display screen some three feet on a side, this is his working surface, controlled by a computer with which he can communicate by means of small keyboards and various other devices.
Farid Mheir's insight:
WHY THIS IS IMPORTANT The fact that AI systems can now suggest new designs and explore new design areas that may be unexplored by human designers should be a strong motivation for businesses to explore how they can leverage AI before someone else does.
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YouTube-BoundingBoxes is a large-scale data set of video URLs with densely-sampled high-quality single-object bounding box annotations. The data set consists of approximately 380,000 15-20s video segments extracted from 240,000 different publicly visible YouTube videos, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. All these video segments were human-annotated with high precision classifications and bounding boxes at 1 frame per second.
Farid Mheir's insight:
WHY THIS IS IMPORTANT Training artificial intelligence systems using deep learning algorithms requires large datasets where the elements you want to recognize - cats, planes umbrellas - are already identified. This dataset from YouTube contains 380000 videos with over 10.5M annotations to help train systems of the future. We have to remember that the more data is available for training the better the system will perform. Nevertheless, this is interesting as is shows a little bit of the behind the scene in AI and helps understand a bit more how good AI systems will be - or when they may fail. Additional datasets are available from google here: https://research.google.com/research-outreach.html#/research-outreach/research-datasets Recently, Google announced an 8M YouTube video dataset to help spark research: https://research.google.com/youtube8m/
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A ranking of the 100 most promising private artificial intelligence companies. It is a purely data-driven/algorithmic process that uses CB Insights data. We’ve gathered this data via our
Farid Mheir's insight:
WHY THIS IS IMPORTANT Are we on the eve of an era where the Fortune 500 is replaced by the "AI 100"? This may be more realistic than you think if the current predictions regarding the future of work push humans to service each other and let all the "work" be done by machines - robots and AI. We are not there yet and this list merely reports 100 startups that are very active in the field AI. Nevertheless, it is interesting to consider what impact AI may have and adjust our digital strategies accordingly... Fortune magazine has also created a very interesting infographics from this mountain of data: http://fortune.com/2017/02/23/artificial-intelligence-companies/
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Maybe you’ve downloaded TensorFlow and you’re ready to get started with some deep learning? But then you wonder: What the hell is a tensor? Perhaps you looked it up on Wikipedia and now you’re more confused than ever. Maybe you found this NASA tutorial and still have no idea what it’s talking about? The problem is most guides talk about tensors as if you already understand all the terms they’re using to describe the math. Have no fear! I hated math as a kid, so if I can figure it out, you can too! We just have to explain everything in simpler terms.
Farid Mheir's insight:
If you want to start nibbling on artificial intelligence, start by reading this tutorial. Simple, yet complete enough to walk you through the most important stuff.
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Curated by Farid Mheir
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WHY IT MATTERS
This article from McKinsey provides an overview of the investments, improvements and priorities in AI and machine learning as of mid 2017.