Fri 2 Feb 2018 07.00 EST Last modified on Fri 2 Feb 2018 07.02 EST YouTube's recommendation system draws on techniques in machine learning to decide which videos are auto-played or appear "up next". The precise formula it uses, however, is kept secret. Aggregate data revealing which YouTube videos are heavily promoted by the algorithm, or how how many views individual videos receive from "up next" suggestions, is also withheld from the public. Disclosing that data would enable academic institutions, fact-checkers and regulators (as well as journalists) to assess the type of content YouTube is most likely to promote. By keeping the algorithm and its results under wraps, YouTube ensures that any patterns that indicate unintended biases or distortions associated with its algorithm are concealed from public view. By putting a wall around its data, YouTube, which is owned by Google, protects itself from scrutiny.

Machine learning isn't a futuristic concept that will have some unforeseen impact far down the road. The technology is already here and impacting business. Data analysis, content creation and better insights into customer data are just some of the ways a company can benefit from machine learning. The other major misconception is that machine learning is only for major companies with massive data sets. Small businesses have much to gain from machine learning as well, given the potential it has for better customer insights and changing the way a business can scale up.

Jeff Bezos, the world's richest man, has added another $20 billion (£14 billion) to his wealth after his firm Amazon reported its largest ever surge in profits. Amazon sales increased by 38 per cent in the last three months of 2017, to more than $60.5 billion (£42.4 billion), according to its latest quarterly financial results. For comparison, it took Amazon more than 14 years to make, cumulatively, as much profit as it did in the latest quarter alone. The profits were driven by sales of its voice-activated Echo devices and an increase in the number of Prime members. They were also helped by an increase in holiday shopping online and by Amazon's cloud business, Amazon Web Services.

Machine learning is no longer confined to the realms of science fiction. It's the reason Google can deliver scarily accurate search results, Facebook's ads are far more appealing to you than they used to be, and your emails aren't full of spam. As the field continues to develop, businesses that want to stay competitive need to keep up with both the potential and limitations of this tech. In this post, we'll take a look at what machine learning is, how it affects you, and examine different use cases. Machine learning is the idea that a machine can make logical decisions and have interactions based on previous experiences in a way that mimics human decisions. Let's backtrack a little and take a look at how most computer programs work. A developer creates a set of rules that tell the machine what to do in a specific situation. The problem with this is that the program relies on the developer's ability to accurately predict a series of future situations. This limits the potential outcomes.

Patients hate when their physician continually looks at a computer or tablet, and this behavior degrades the physician-patient relationship. Smart speakers facilitate charting data and accessing information while continuing to make eye contact with the patient. Accessing information in the sterile operating room is cumbersome, and surgeons are essentially robbed of the ability to use standard desktop/smartphone technologies. This is solved beautifully by the smart speaker. Healthcare providers face crushing workloads, and there is a premium on immediate information accessed seamlessly.

Machine Learning (ML) is reshaping our lives. From 10 million self-driving cars by 2020 to self-tuned databases, automated surveillance systems, smart assistants like Siri and humanoids like Sophia, it is everywhere. ML is making the devices, gadgets and apps smarter like humans, empowering them to take decisions on their own and facilitate us with a better experience. Besides this, Allied Market Research has predicted that Machine Learning as a service market will reach $5,537 million in 2023 while growing at a CAGR of 39.0% from 2017-2023. All these significant numbers have prompted the startups and established brands to turn towards the Machine Learning.

How AI helped Walmart go from 700,000 to 60 million items online. Target shoppers can now use the AI-driven Pinterest Lens visual search tool to find products with ease, using Target's e-commerce site and mobile app.2O'Shea, Online homeware store Wayfair has also adopted AI and machine learning for image-based search.3Terrelong, What the future of retail will look like, according to Google. The power of voice: Voice-assisted ordering for browsing and buying will be big this year, led by tech titans Amazon, Google and Apple. Amazon Echo accounts for approximately 75% of the smart speaker market, and more than 25% of searches now take place using voice-enabled devices, making connected homes a priority for retail tech leaders Amazon, Google, and Apple.6Duggan,

Frank Reeves, co-founder and CEO from Avvio, one of the many super-smart travel tech businesses set up in Ireland, sat down with tnooz to explain how the machine learning capabilities of AI can help hotels address one of their biggest headwinds – conversion rates. Frank: The difference between a standard booking engine and AI-driven booking engine is that an AI engine will actively learn from every website interaction between a hotel and its website visitors, and learn how to optimise that sales conversation and that brand experience for the customer, in order to improve conversion rates…It also uses that learning to improve its own performance. Perhaps even more interesting than Allora's ability to learn at the individual hotel level is the fact that it can learn across the network of hotels that use Allora. Take the example of an independent hotel web site which, says, gets five instances a year of a Japanese customer on a smartphone trying to book a room. Of course Allora will try to optimise each of these visits, but five is a small number.

Google has been improving its Home devices for a while now. The company added better search, upgraded the Home app interface and enabled an intercom feature last November. It can also now match your voice to your own Netflix profile, too, a feature that builds upon Home's multiple voice recognition system. Now Google has added an update that adds a voice-powered alarm function and makes it a bit easier to find shows and music with your Home devices. If you link your Netflix account with Google Home, you can tell it to watch any of the streaming platform's shows with a voice command.

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