2017 has been a booming year for the field of artificial intelligence. While A.I. and data-focused machine learning have been around for decades, the algorithmic technologies have made their presence known in a variety of industries and contexts this year.

Microsoft UK’s chief envisioning officer Dave Coplin has called A.I. “the most important technology that anybody on the planet is working on today,” and Silicon Valley companies seem to have taken that to heart: They’ve been hiring A.I. experts right and left, and with those in short supply, they’ve started teaching employees the fundamentals of A.I. themselves.

Not every A.I. achievement has been met with admiration and applause, though. Some are worried about the human prejudices that are being introduced into A.I. systems. ProPublica found in 2016, for example, that the software algorithms used to predict future criminals were heavily biased against black defendants. And earlier this year, Facebook came under fire for the algorithmically generated categories advertisers could use to target users, which included hateful groups and topics such as “Jew hater.”

Situations like these have prompted experts to urge companies and developers to be more transparent about how their A.I. systems work. However, in many other cases—especially of late—A.I. has been used to good end: To make discoveries, to better itself, and to help us expand beyond the limits of our human brains.

A.I. Spotted An Eight-Planet Solar System

Successful astronomical discoveries often center around studying data—lots and lots of data—and that is something A.I. and machine learning are exceedingly good at handling. In fact, astronomers used artificial intelligence to sift through years of data obtained by the Kepler telescope to identify a distant eight-planet solar system earlier this month. This solar system now ties our own for the most known planets circling its star, in this case Kepler-90, located more than 2,500 light years away.

From 2009 to 2013, the Kepler telescope’s photometer snapped 10 pixel images of 200,000 different stars every half hour in search of changes in star brightness. If a star dimmed and brightened in a regular, repeating pattern, that could be an indication that it has planets orbiting.

(You can also use that information to estimate the size and length of orbit of a planet circling a particular star.) University of Texas at Austin astronomer Andrew Vanderburg and Google software engineer Christopher Shallue developed the neural network that made the discovery using 15,000 known exoplanet indicators.

They zeroed in on 670 stars with known exoplanets, but focused specifically on weak signals—smaller exoplanets previous researchers may have missed. The planet the duo discovered, dubbed Kepler-90i, appears to be the third planet orbiting its star, much like our own Earth.

Beat The World Champion Go Player

Google’s DeepMind researchers developed an A.I. that plays the ancient, complex Chinese strategy game of Go. The initial version defeated the world’s best Go player in May, but that wasn’t enough. A few months later, Google developed a new version of this AlphaGo A.I.: AlphaGo Zero. This A.I. achieved a superhuman-level Go-playing performance—it beat the original AlphaGo A.I. 100 to 0.*

Bested Poker Pros at No-Limit Texas Hold’Em

An A.I. developed by Carnegie Mellon’s computer science departmentrecently beat professionals at one of the most difficult styles of poker, no-limit Texas Hold’em. Unlike strategy games like chess and Go, poker is what’s considered an “imperfect-information game” because the player must make decisions even as some information is hidden.

On top of that, it’s not just making moves—it’s knowing when to bluff, too. In a 20-day competition with a $200,000 prize pool and 120,000 total poker hands played, Carnegie Mellon’s A.I., Libratus, beat the world’s top poker professionals.

 

And Taught Itself To Program

Artificial intelligence not only made some notable discoveries and competitive successes this year.

It also excelled in a different area—making its programmers obsolete. We exaggerate: Several different artificial intelligence programs (including ones developed by Google, Microsoft, and Facebook) learned how to write basic code at a level that could help non-programmers with complicated spreadsheet calculations or reduce some of the tedium that experienced developers have to deal with.

Microsoft’s A.I., DeepCoder, might be considered the most basic of the three, although it’s still an incredibly complicated feat. This A.I. can understand a mathematical problem you need to solve, look through existing examples of code for similar problems, and then develop a code-based solution.

DeepCoder could eventually be useful for those who can’t or don’t want to learn to code but need to use a code-based solution for computations (for example, tricky spreadsheet calculations). The solutions are relatively simple and, in terms of solution and structure, are based on situations the A.I. has experienced before. They usually end up being between three and six lines of code total.

Google’s program, in contrast, taught itself to program machine learning software and, in one case, learned to recognize objects in photos—a much more challenging task. Named AutoML, the program ended up achieving a 43 percent success rate at its task—4 percentage points better than the code developed by its human peers. AutoML’s biggest benefit, though, is in automating the process of developing machine learning models, a process that’s normally time consuming for human machine learning experts.

And then there’s Facebook’s self-taught chatbots, which fall on a slightly different scale of self-taught abilities. The two A.I. agents, Bob and Alice, started out speaking in English but then...developed their own language to speak in. “Agents will drift off understandable language and invent codewords for themselves,” said Dhruv Batra, visiting research scientist from Georgia Tech at Facebook A.I. Research, in an interview with FastCo Design.

While this got a lot of blowback in the press (“creepy” was a common headline descriptor), it’s actually a fairly common occurrence. A.I. systems evolve using a rewards-based system, and if there’s no benefit from a particular course of action, they’ll try something else instead. Still, the Facebook researchers eventually shut down the A.I. bots since their goal was to create entities that will eventually interact with people—there was no Her-style ending for these digital acquaintances.

*Correction, Dec. 28, 2017, at 4:40 p.m.: This post originally misstated AlphaGo Zero had beaten the original AlphaGo 100 to 1. It actually beat it 100 to 0.

 Future Tense is a partnership of SlateNew America, and Arizona State University.

 Christina Bonnington is a technology writer whose work has appeared in WiredRefinery29, the Daily Dot, and elsewhere.

This article was published by Slate.com.

I often try to switch my own lens, imagining myself as a doctor or a professor, in order to understand the potential of my own products. Artificial intelligence (AI) is the perfect example of how something new could be used to change every aspect of our lives when we change the lens. And education is an area that has unlimited potential to utilize innovation. The ability to tap into new technologies to enhance and accelerate the learning process can streamline everything from admissions and grading to student access to vital resources. 

Automating and Expediting Administrative Tasks

One of the simplest but impactful things AI can do for the educational space is to speed up the administrative process both for institutions and educators. The tedious process of grading homework, evaluating essays and measuring student responses can require valuable time from lecturers and teachers who would prefer to focus on their lesson planning and one-on-one time with students.

Machines are already capable of automating the grading process for multiple choice and fill-in-the-blank tests. Soon, they will be able to assist and eventually replace human grading for written response work as well. Admissions processes can also be streamlined and improved, reducing the workload for high volume admissions offices. Automating the process of paperwork and support for students with common admissions questions via chatbot and interactive website materials can improve the process for both administrators and future students.

 Outside the Classroom Support

Until very recently, students were forced to rely on their teachers and parents, who have limited time and availability, when assistance was needed. Tutoring and additional educational support couldn’t be guaranteed at all grade or socioeconomic levels. Through AI, tutoring and study programs are growing more advanced, capable of teaching fundamentals to students struggling with basic concepts.

Already, there are intelligent tutoring systems such as Carnegie Learning that use data to provide feedback and work with students directly. These tools are designed to support teacher and tutor approaches to student difficulties but soon will be more advanced and capable of providing specific details for students as well.

 In the future, visual and dynamic learning channels outside the classroom will become not only more prevalent but capable of supporting a range of learning styles, all while addressing common questions and concerns students have that cannot be readily addressed by teachers, TAs, tutors or parents.

Adaptability of Assessment and Educational Support Software

Individualized learning offers many benefits for students with an array of learning styles. But in many settings, it’s not a viable solution. One teacher with 20 or more students has limited time and resources to customize the curriculum.

AI offers an opportunity to tap into the adaptive learning processes already being featured in assessment software, learning games and digital textbooks to individualize learning. Tools that can highlight and emphasize key areas where students are suffering allow teachers to focus on facilitating the learning process and offer the one-on-one support that students at all levels need.

Where and How We Educate

One of the primary challenges of integrating AI into the educational system is the natural impact it will have on the very nature of education. Where and how will we interact with students if we start relying more heavily on machines as educational resources than teachers?

 Already, the number of online classrooms has exploded in recent years. In 2016, 28 percent of students surveyed by Online Learning Consortiumreported taking at least one online class. And the rate has increased consistently year over year, with many students attending college strictly online. Classrooms still serve a vital purpose in introducing students to new ideas and developing critical problem-solving skills that are hard to replicate in a machine setting. But the one-size-fits-all nature of compulsory education will almost certainly change as AI becomes more prevalent.

Students in MOOCs, online learning classes and blended class experiences will be able to benefit from customized, individualized learning paths in ways that until recently would not have been possible.

The Shifting Face of Curriculum Materials

Already there are AI tools that use the basic outline of a course and syllabus provided by the teacher to produce customized textbooks and curriculum for students. These systems will only continue to advance as the technology running them becomes more sophisticated.

Curriculum materials heavily rely on access to digital devices in the form of laptops and iPads, both in and out of the classroom. New software that customizes the tools and resources available on these devices will make it possible for teachers to provide a framework in which students actively engage with individualized resource materials.

Immersive Technology in the Classroom

Hands-on education is a challenge. It can be time-consuming and with a large group of students, prove difficult to manage. Homework, which requires students to take an active role in their education, works to some degree as experiential learning, but it is often considered boring. How do we overcome that challenge?

 Technology offers exciting new opportunities in the form of immersion that rely on new wearable devices like the Microsoft HoloLens or augmented reality now being integrated in smart devices. Being able to see and interact with the human body or a microscopic cell instead of just reading about it in a book -- with the supplement of an AI providing tangible, individualized learning support -- will transform how students engage with the material.

The Future of Education Is Here

There are startups in the education space like Clever that are taking on education in this way already and countless more trying to break in. With this vested interest by the startup community, it’s no doubt that change is happening and AI will become more integrated very soon. AI will be a major part of that, as students, teachers and administrators all benefit from a smarter, more personalized approach to education.

 Post Written By : Adrien Schmidt to Forbes.

CEO of Bouquet and Co-Founder of Squid Solutions, a successful entrepreneur, engineer and innovator.

 

Artificial Intelligence or AI is the intelligence developed by humans, which works on computers.  From Siri and Google Assistant in your mobile phones to the chat bot on your favorite social media, which gives you instant replies work on Artificial Intelligence.

Artificial Intelligence has a major role in making the world go forward. As of 2017 Artificial   Intelligence is one of the major sectors were major companies are investing their money in.

From google to the startup that just sprung up near your town each and every company is utilizing Artificial Intelligence. Artificial   intelligence has limitless possibilities. Saudi Arabia gave citizenship to a robotic few month back, which was running on Artificial Intelligence. Major companies invest in stock markets, according to the predictions done by Artificial Intelligence bots.Every prediction done using Artificial Intelligence was pin point correct and created huge profits for the companies.

Artificial Intelligence has now turned out to be an inevitable factor. We all are exposed to Artificial Intelligence but haven’t understood or utilized it to its full potential. The invention of Artificial Intelligence has revolutionized our lives.

From medical field to online shopping Artificial Intelligence has secured its place everywhere in our day to day life.Helping doctors perform critical surgeries to helping an ambulance driver to reach a dying patient on time Artificial Intelligence has turned out to be an invention that will change the human race just like the invention of fire did. Most of the tech giants utilize Artificial Intelligence.

Artificial Intelligence is now being used to predict future based on calculations and researches done on past data. So far there hadn’t been any reports on a failed AI theory. The AI is also helping scientists and doctors find medicines for incurable diseases and other issues human body faces.

Artificial Intelligence is revolutionizing our earth. AI is now being used to predict climate changes and prevention methods. AI is moving to a stage where it could replace a lot of human jobs and easing human labor.

AI is now the perfect solution for selection processes since it can select the best from a group of objects if you feed in the qualities you require. Artificial Intelligence may also be dangerous if it’s not controlled.

AI in the wrong hands may be terrifying as well. AI is also being used by militaries all over the world in order to analyze situations before making serious decisions. AI is going to upgrade the Human Species and a revolution is coming. It has already begun and will soon reach one of us and transform our life.

One of the big confusions we all has was what is the difference between Artificial Intelligence AI,Machine Learning ML and Deep Learning DL. As these terms are maturing today some of the definitions of these terms are not still concrete and there is a lot of media hype where people use these terms interchangeably.

In order to clarify and throw some light at what is the difference between these terms. These definitions ad differences discussed in this article may not be hundred percent accurate since most of these areas are vastly developing.

Let’s begin with Artificial Intelligence  AI. AI is basically is in a nutshell which enables computers to think. There has been various stages of AI since almost early 1950’s. Artificial Intelligence AI is now the broad area which enables computers to think. Machine learning is a sub area inside AI.

ML are a bunch of statistical tools to learn from data. Which means ML is a subset of AI or a part of AI. Coming to Deep Learning DL, DL is a much more recent area which has taken shape since 2006. Dl is all about using something called Multi Layered Neuro Networks. A huge impact of AI has been occurring in DL. For all of these terms there are mathematical tools. There are tools in probability, tools in statistics, tools in Linear Algebra and Matrix Algebra etc. These are all fundamental mathematical areas and there is of Course computer programming. Without computer programming and algorithm you can’t train computers to think.

For a long time we were trying to replicate our thoughts by giving a lot of rules to computers. And we thought as a result, we could build an AI and what it turned out to be is a terrible way. Instead, researchers started to create algorithms that learn from themselves. And it was the beginning of ML. Hence ML is the subdivision of AI which enables machines to learn from data without explicitly programmed instructions and rules.

For example, suppose that you have to build an algorithm that can play tic tac toe. We have two cases in the AI case, you would have to take it from its hands and give it many logical rules to learn how to play. As a result you cannot build a machine that is good at tic tac toe without being good at it yourself. In the machine learning case you may not know how to play tic tac toe by yourself. Yet you can come up with a great software. Unlike the AI case you give it many examples of previous games and let it learn the rules by itself. Hence today ML is providing the tools the industry and society can use.

Deep Learning DL is the subset of Machine Learning ML. Consider it as the cutting edge of the cutting edge. Like in ML the data is fed through neural networks which are algorithms that take inspiration from the human brain and those neural networks will extract a numerical value called output for every data. It may be audio, video or images. And then it will classify it as data. For example I can tell whether a given picture is a cat or not.

In ML case we would have to define features such as if the animal has ears. And if yes, then if they are pointed. We would have to define all the facial features and let the system learn which features are the most important to classify a specific animal. But deep learning takes a step ahead and automatically finds out the features that are important for classification. Well, that’s what we could gather up for you on AI, ML & DL.

 

Image credit: Nvidia,Google.

Here is a list of recent product releases and updates for December, from companies that offer services to online merchants. There are updates on email marketing, same-day delivery, one-click purchasing, e-gifting, sensory branding, and shopping from your car’s dashboard.

 

 

Ecommerce Product Releases

Magento Commerce launches one-click instant checkout. Magento Commerce, the ecommerce platform, has announced the launch of Instant Purchase, becoming the first commerce platform to deliver the functionality since the Amazon 1-Click patent expired in September 2017. The new capability helps merchants drive repeat purchases for returning customers with a streamlined checkout process. The Instant Purchase feature was developed in conjunction with enterprise solution partner Creatuity and is now available.

 

Amazon expands Prime delivery services. Amazon has expanded Prime Free Same-Day Delivery and Prime Free One-Day Shipping to members in more than 8,000 cities and towns. To find eligible items, Prime members can look for logos next to product listings or filter using the checkbox located in the filter menu. Select free same-day or one-day shipping at checkout and eligible orders over $35 will be delivered for free. Eligible same-day orders placed in the morning arrive from 6 – 9 p.m.; orders placed in the evening or through the one-day option arrive the next day.

GM launches Marketplace for in-car shopping and reservations. General Motors has announced that it will be equipping new cars with an in-dash ecommerce system for drivers to order food, find fuel, and reserve hotel rooms. Drivers will be able to transact by tapping icons on the dashboard (rather than a smartphone) while driving. Developed with IBM, the Marketplace technology will be available in 4 million vehicles by the end of 2018.

Target adds e-gifting option to website. Target has unveiled a new feature on its website called “GiftNow,” letting shoppers pick out items that are sent via email to gift recipients. A recipient can then accept the gift by providing a shipping address, change the size of the item, or even exchange it for another product.

Payment Rails launches Global Payout API to 220 countries. Payment Rails, an API-first payout platform, has announced the launch of a global payout platform for sending payments to roughly 220 countries in approximately 135 currencies. Through a single simplified integration, businesses and platforms can now access global banking, payment, and real-time networks around the world in a fully compliant and regulated environment. The Payment Rails platform has no setup fees, monthly minimums, or long-term contracts.

Visa announces sensory branding. Visa has announced a sensory branding suite that will support the Visa brand in an expanded universe of connected, payment-enabled devices. These new sound, animation, and haptic (vibration) cues will help signify completed transactions in digital and physical retail environments when consumers pay with Visa. The sound of Visa will debut in Visa’s global advertising campaign ahead of the Olympic Winter Games PyeongChang 2018.

Prime Now and Amazon Handmade team up to offer fast delivery for local artisans. Prime Now, Amazon’s one and two-hour delivery service, and Amazon Handmade, a site for handcrafted items, have announced that unique, handcrafted products from local artisans are now available on Prime Now. This is the first time Amazon Handmade Artisans is using Amazon’s fastest delivery method to extend the selling season through Christmas Eve on Prime Now. Amazon Handmade products are available through the holiday season via Prime Now for members in Austin, Brooklyn, Manhattan, Minneapolis, Phoenix, Portland, Raleigh, San Diego, San Francisco Bay area, and Seattle.

First contemporary art purchased with Bitcoin in U.S. American artist Mark Flood’s painting was the first art piece to be purchased with Bitcoin in the U.S. It sold last week in New York for 12.3 BTC (equivalent to $100,000) by The White Company, a purveyor of fine art and luxury goods that specializes in working with clients in the cryptocurrency space. “Select a Victim,” a 2013 painting by Flood, was offered privately to an anonymous Canadian citizen. The White Company was started for consumers who hold wealth in cryptocurrencies to purchase luxury goods without giving up their desire for anonymity.

Shopventory officially launches on Shopify app store. Shopventory, a provider of inventory management, sales reporting, and optimization solutions for small-to-medium size businesses, has announced its formal launch on the Shopify app store of its Shopventory inventory and channel management platform. Shopify users can now easily push their Shopify catalog to a multitude of point-of-sale providers, such as Square, Clover, and PayPal Here while mobile point-of-sale users can expand their online offering by sharing their POS catalog with the Shopify platform.

Heap introduces a new platform to derive customer insights. Heap, an analytics provider, has announced a process to automate all phases of customer insights. Heap’s autonomous customer insights platform has three key layers: data capture, control, and insights. The data capture plane automatically captures all behavioral data from sources across departments and domain-specific tools into one standard schema. The control plane assures data integrity and the ability to change event definitions on the fly; and the insights plane produces networked insights across marketing, sales, and customer success silos.

Dynamic Yield launches a complete suite for email personalization. Dynamic Yield has announced the launch of a full suite of email personalization capabilities, enabling marketers to deliver personalized, engaging emails to their customers. Dynamic Yield for Email is built into Dynamic Yield’s core personalization suite so marketers can leverage user data and preferences across all customer channels to deliver true personalization in email. Personalize and synchronize the entire customer journey from start to finish including web, mobile web, mobile apps, campaign landing pages, advertising and now, email.

 

Source: Practical Ecommerce, SIG UELAND.

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