They arguably have one of the best occupations in the world. But whisky sommeliers may soon have some competition for their jobs – from AI. Scientists have devised machine learning algorithms that can determine whether a whisky is of American or Scotch origin and identify its strongest aromas. And they even outperform human experts, the results show. A whisky's aroma is determined by a complex mixture of odorous compounds, which makes it highly challenging to assess. Panels of human experts are often used to identify the strongest notes of a whisky but these require a significant investment in time, money and training – and agreement between experts is often rare.

While whisky is one of the most popular tipples around the world, most drinkers still rely on the label to distinguish between cheap and expensive bottles. Now, scientists have developed a robotic nose called NOS.E that can distinguish between different brands, origins and styles by'sniffing' the liquor. During testing, NOS.E reached 100 per cent accuracy for detecting the region, 96.15 per cent accuracy for brand name and 92.31 per cent accuracy for style. The team hopes the bot could be used across a wide range of beverages in the future, including wine and cognac, as well as whisky. Scientists have developed a robotic nose called NOS.E that can distinguish between different brands, origins and styles of whisky by'sniffing' the liquor NOS.E is designed to mimic the human olfactory system and uses eight gas sensors to detect odours in a vial of whisky.

Today I will present a guided tutorial for applying Kemp & Tenembaum's brilliant "form discovery" algorithm to a wine dataset. Ultimately, this provides a data-driven map to choose wines from, based on our tastes. If you are, like me, fond of data science, machine learning, cognition and/or a wine lover, then you might find this post interesting. Actually, if you know of ways it could be improved I'd love to hear them!] First of all, like every recipe, we'll start with a list of things we need: Essentially, in their work Kemp & Tenenbaum created an algorithm which finds the best structural representation for a dataset, without any assumption nor indication about this dimension.

Spotted: Through AI technology, the wine storage wall works as an intelligent unit that gives serving suggestions as a sommelier would, helping the owner to organise and maintain their wine collection. A high-speed 8-axis robotic arm aids this, and is programmed to apply minimal pressure in order to protect the bottle which it recognises, loads, scans and dispenses. Three cameras track the movement of the arm from the moment it selects the bottle, to the dispensing, all of which takes 15 seconds. Other facets of the wine wall include facial recognition software, which helps to give and withhold access to either the whole collection or even single, special bottles. Motion sensors are also able to detect unusual movement around the wine, for extra security.

What do you get for the person who has everything? How about an artificially intelligent robot sommelier that can securely store, manage and suggest wines from your collection? The Winecab Wine Wall does all that (hat tip to Boss Hunting), acting kinda like a very expensive automated wine vending machine that you'd find in only the poshest 7-Eleven. Wine Walls come in a variety of sizes, from the more modest Curio Classic model, which holds 130 bottles ($139,000) to the 15 ft. Wine Wall, which holds 600 bottles ($249,900).

One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset. This process can be difficult, time consuming and often requires a great deal of domain knowledge. We present Sommelier, an expert system for recommending the machine learning algorithms that should be applied on a previously unseen dataset. Sommelier is based on word embedding representations of the domain knowledge extracted from a large corpus of academic publications. When presented with a new dataset and its problem description, Sommelier leverages a recommendation model trained on the word embedding representation to provide a ranked list of the most relevant algorithms to be used on the dataset. We demonstrate Sommelier's effectiveness by conducting an extensive evaluation on 121 publicly available datasets and 53 classification algorithms. The top algorithms recommended for each dataset by Sommelier were able to achieve on average 97.7% of the optimal accuracy of all surveyed algorithms.

If AI is to become a meaningful facet of society, identifiable and understandable by consumers, its value must be articulated. And for that to happen, designers of AI-driven experiences must make the invisible visible; they have to give AI a good, old-fashioned brand identity. A skeptic might wonder why AI needs branding in the first place. If it's meant to silently toil away in the background of our lives, why does it need to announce itself? Why give consumers yet another thing to think about?

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Lots of people keep their Amazon Echo or other Alexa-enabled smart speaker in the kitchen, mostly because it's a central location in the home. However, this is actually a great place to use Alexa, as she has a host of under-appreciated kitchen skills. Sure, you can set timers and convert measurements with Alexa, but she can do so much more than that--from finding you recipes to maintaining your grocery list and suggesting wine pairings.

It smells like strong, hard plastic, like perm solution. Yes, the melted plastic carries through on the finish. That wine, I won't name names, did not make the cut in Wine Access' super-selective, scientific wine judging panel, which I sat in on last month at Napa's The Kitchen Collective. The panel is composed of three experts: Matt Deller, one of only 45 Masters of Wine in the U.S.; Sur Lucero, who passed all three sections of his Master Sommelier exam on the first attempt and has worked in restaurants like The French Laundry and Meadowood; Vanessa Conlin, a Master of Wine candidate who has passed the theory portion of her exams. To say the scene was intimidating, would be a giant understatement.

If you've ever been handed a wine list the size of an encyclopaedia in a posh restaurant and succumbed to mild panic, you're not alone. Many of us feel sweaty palmed when having to choose from a bewildering array of wines we've never tasted or even heard of, especially when we're trying to impress a hot date or a potential client. And traditionally snooty sommeliers - wine advisers to the uninitiated - trying to embarrass us into spending more than we can afford, only make matters worse. Luckily there are a growing number of wine apps offering to help us navigate the worrying - but wonderful - world of wine. "I used to hate having to choose wine in restaurants... it was horrible," says Matt Gertner, the Prague-based founder of wine app start-up Corkscrew.