The Alibaba (BABA) versus Amazon (AMZN) contest is getting more interesting. Alibaba is now convinced that Bezos' Alexa-enabled Echo smart speaker is really an effective way to compel more people to do repeat online purchases. It understood why Apple (AAPL), Google (GOOG) (NASDAQ:GOOGL), and Samsung (OTC:SSNLF) all came up with copycat versions of the Amazon Echo. Jack Ma also probably believed RBC Capital Markets' projection that the Echo speaker could generate $10 billion in business for Amazon by 2020. Even though Alibaba is a Business-to-Business [B2B] e-commerce operator, it is now selling its own $73 smart speaker. I do not know the exact details regarding how Alibaba will implement the cloud Artificial Intelligence component in the Tmall Genie.

We're often puzzled as to how people can use terminology to put an entirely new coat of paint on something that's been around for a long time and then purport that it's new and exciting. Take robo advisors as an example. In the good old days, you met with a wealth manager who asked you a bunch of questions and then plugged them into a piece of software that said where you should optimally invest your money. Usually, this is just a very simple asset allocation strategy with X% in bonds and Y% in stocks based on your age and appetite for risk. Then, as a result of the perpetual cost savings initiatives that permeate the corporate world, we decided to remove the human element, externalize the software, and call it a "robo advisor", a term that somehow implies that there is more under the hood than there actually is.

The HR industry needs to move at a very fast pace, in order to respond to shifting social and technological paradigms in today's fast paced digital world. Today, employers and job seekers alike have different needs and strive to connect in different ways. This article focuses on 5 ways in which AI tools are currently shaping the HR industry. Read on to find out more. Intelligent assistants: Also known as "Chatbots" are becoming widely popular in the HR industry.

Spotify's music recommendation engine – Spotify have built their own AI and machine learning engine, whose sole purpose is monitor a user's listening preferences and tailor the whole product around it. In the first 2-3 weeks Spotify's AI engine would have understood what you like, which artists are your favorite, what are the genre's you are most interested in, to minute details like, when do you listen most often, your average listening period, etc. Using all these data, the AI engine will recommend music that you are most likely to favorite or save to your own collection. Apart from that, it will automatically send you communication about new releases from your favorite artists, how your favorite music charts are shaping up, etc. Streaming industry experts, credit Spotify's AI engine for their massive growth in last 6 years and also how easily they have fended off bigger companies like Apple, Google, etc in the same industry to keep the top player title for themselves.

Alibaba launching the TMall Genie digital-assistant speaker, similar to Amazon's Alexa, as the Chinese e-commerce powerhouse becomes the latest to offer an artificial intelligence device for the home.

Welcome to "What Is," a new column on dedicated to giving simple answers to complex things that affect the products around you. Submit yours to [email protected] and we'll do our best to get them answered. Every day we hear buzzwords like artificial intelligence and machine learning at some point or another in the news cycle, typically associated with smartphones, smart speakers, drones, and so on. It's easy to take for granted that these are technologies that make our lives easier and more convenient, undergirding everything from facial recognition to autocomplete on Google. Even Spotify uses machine learning for music recommendations.

In the book, Gates predicted: "Automated price comparison services will be developed, allowing people to see prices across multiple websites, making it effortless to find the cheapest product for all industries." Those "small devices" are now smartphones, tablets and smartwatches, which allow users to check the weather, news, stock prices and book appointments. This is now true, since people can pay their bills online, check their bank accounts through apps and can communicate with doctors through web portals, or book appointments with the ZocDoc app. Assistants like Apple's Siri, Amazon's Alexa, Microsoft's Cortana and Google Assistant alert people about events on their calendar, orders items online for them and reminds them about their groceries.

This time we are a satellite event of Tech Open Air and are really glad to be part of this great conference. We are going to have different speakers from Helvetia, Rasa and more to be announced. What will you learn about? Helvetia will talk about on how they have integrated a bot into their processes. Rasa is going to show why their open source framework helps you to get started to build more intelligent bots.

Alibaba is entering the fray of digital speaker assistants in a bid to compete with Amazon The Chinese company unveiled the Tmall Genie X1,' which will go for about $73. Jack Ma, Chairman of Alibaba Group, speaks with reporters following his meeting with President-elect Donald Trump at Trump Tower, January 9, 2017 in New York City. Alibaba on Wednesday unveiled a new AI-powered smart speaker due to hit the market in August. There's a big new player in the competition to infiltrate homes with smart speakers: Chinese online sales giant Alibaba. The e-commerce company unveiled its Tmall Genie speaker Wednesday in Beijing.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.