Down the road
The end game for quantum computing is a fully functional, universal fault-tolerant gate computer. To fulfill its promise, it needs thousands, maybe even millions, of qubits that can run arbitrary quantum algorithms and solve extremely complex problems and simulations.
Before we can build a quantum machine like that, we have a lot of development work to be done. In general terms, we need:
Hotel revenue management and use of analytics for room sales has remained largely unchanged for decades since the early 1980s when hotels started looking at yield and how they could optimize the revenue each room could generate. By the mid-1990’s, Marriott’s successful execution of revenue management strategies were adding between $150 — $200 million in annual revenue and thus marked the beginning of data intelligence to drive new revenue.
Fast forward to 2016 — and the part insight, part intuition, part data-driven approach to revenue management largely hasn’t moved into the new age of big data for most hoteliers.
There is a new application of data modelling hotels are utilizing to see big gains in RevPAR (Revenue Per Available Room) and this comes through price differentiation. That is — dynamically displaying different room rates for every person that views your hotel search price query.
This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)
The groundwork for machine learning was laid down in the middle of last century. But increasingly powerful computers – harnessed to algorithms refined over the past decade – are driving an explosion of applications in everything from medical physics to materials, as Marric Stephens discovers.
A flamingo lives 40 years and a human being lives 90 years; a mouse lives two years and an elephant lives 60. Why? What determines the lifespan of a species? After analyzing nine species of mammals and birds, researchers at the Spanish National Cancer Research Center (CNIO) found a very clear relationship between the lifespan of these species and the shortening rate of their telomeres, the structures that protect the chromosomes and the genes they contain. The relationship is expressed as a mathematical equation, a formula that can accurately predict the longevity of the species. The study was done in collaboration with the Madrid Zoo Aquarium and the University of Barcelona.
“The telomere shortening rate is a powerful predictor of species lifespan,” the authors write in the prestigious journal Proceedings of the National Academy of Sciences (PNAS).
The study compares the telomeres of mice, goats, dolphins, gulls, reindeer, vultures, flamingos, elephants and humans, and reveals that species whose telomeres shorten faster have shorter lives.
Algorithms meant to spot hate speech online are far more likely to label tweets “offensive” if they were posted by people who identify as African-American.
AI systems meant to spot abusive online content are far more likely to label tweets “offensive” if they were posted by people who identify as African-American.
The news: Researchers built two AI systems and tested them on a pair of data sets of more than 100,000 tweets that had been annotated by humans with labels like “offensive,” “none,” or “hate speech.” One of the algorithms incorrectly flagged 46% of inoffensive tweets by African-American authors as offensive. Tests on bigger data sets, including one composed of 5.4 million tweets, found that posts by African-American authors were 1.5 times more likely to be labeled as offensive. When the researchers then tested Google’s Perspective, an AI tool that the company lets anyone use to moderate online discussions, they found similar racial biases.
A hard balance to strike: Mass shootings perpetrated by white supremacists in the US and New Zealand have led to growing calls from politicians for social-media platforms to do more to weed out hate speech. These studies underline just how complicated a task that is. Whether language is offensive can depend on who’s saying it, and who’s hearing it. For example, a black person using the “N word” is very different from a white person using it. But AI systems do not, and currently cannot, understand that nuance.
The art of matchmaking has traditionally been the province of grandmas and best friends, parents, and even—sometimes—complete strangers. Recently they’ve been replaced by swipes and algorithms in an effort to automate the search for love. But Kevin Teman wants to take things one step further.
The Denver-based founder of a startup called AIMM has built an app that matches prospective partners using just what they say to a British-accented AI. Users talk to the female-sounding software to complete a profile: pick out your dream home, declare whether you consider yourself a “cat person,” and describe how you would surprise a potential partner.
At first glance, that doesn’t seem too different from the usual swiping-texting-dating formula of modern online romance. But AIMM, whose name is an acronym for “artificially intelligent matchmaker,” comes with a twist: the AI coaches users through a first phone call, gives advice for the first date, and even provides feedback afterwards. Call it Cyrano de Bergerac for the smartphone era.
Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in the brain. Using the speed of modern computers and large datasets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience.
Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artificial intelligence algorithms—based on the very slow brain dynamics—which outperform learning rates achieved to date by state-of-the-art learning algorithms.
In an article published today in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years.
This video is the ninth in a multi-part series discussing computing and the second discussing non-classical computing. In this video, we’ll be discussing what quantum computing is, how it works and the impact it will have on the field of computing.
[0:28–6:14] Starting off we’ll discuss, what quantum computing is, more specifically — the basics of quantum mechanics and how quantum algorithms will run on quantum computers.
[6:14–9:42] Following that we’ll look at, the impact quantum computing will bring over classical computers in terms of the P vs NP problem and optimization problems and how this is correlated with AI.
[9:42–14:00] To conclude we’ll discuss, current quantum computing initiatives to reach quantum supremacy and ways you can access the power of quantum computers now!
