Within six months of implementing the algorithm, it increased Rue La La's revenue by 10 percent. Simchi-Levi's process involves three steps for generating better price predictions: The first step involves matching products with similar characteristics to the products to be optimized. The second step requires testing a price against actual sales, and adjusting the product's pricing curve to match real-life results. For deals with fewer bookings per day than the median, the average increase in revenue was 116 percent, while revenue increased only 14 percent for deals with more bookings per day than the median.
Some also use it to send text messages through voice commands while driving, or to communicate with a speaker of another Chinese dialect. But while some impressive progress in voice recognition and instant translation has enabled Xu to talk with his Canadian tenant, language understanding and translation for machines remains an incredibly challenging task (see "AI's Language Problem"). In August, iFlytek launched a voice assistant for drivers called Xiaofeiyu (Little Flying Fish). Min Chu, the vice president of AISpeech, another Chinese company working on voice-based human-computer interaction technologies, says voice assistants for drivers are in some ways more promising than smart speakers and virtual assistants embedded in smartphones.
The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.
Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.
These days, it’s tough to avoid newspaper headlines warning that artificial intelligence is coming for your job. The problem is that, often, the only thing these oversimplifications get right is that there is in fact an important connection between automation and work. What’s surprising is how many examples there are of AI acting as the catalyst for new hiring, higher wages, and happier employees. But of course AI success stories aren’t as exciting as the “job-stealing robots” narrative. The reality is that the impact of AI on the workforce is complex, nuanced, and still very much in transition.
From improved key performance indicators like customer conversion to better results in user-behavior-based metrics such as engagement rates, both chatbots and AI bots provide a foundation for sustainable business growth through improved user experiences, scalability, and low overhead, high return efficiency. Regardless of whether a business uses focalized chatbot technology or more advanced AI bots, businesses can advance the user experience with bots while managing expenses. Regarding specific metric growth, chatbots and AI bots help users to achieve desired actions, meaning a lift in conversion, one of the most vital metrics to master. Bots in customer service retain customers through shortened wait times and available user assistance to get customers through the sales process and other actions easily.
During training, a neural net continually readjusts thousands of internal parameters until it can reliably perform some task, such as identifying objects in digital images or translating text from one language to another. But, at the 2017 Conference on Empirical Methods on Natural Language Processing starting this week, researchers from MIT's Computer Science and Artificial Intelligence Laboratory are presenting a new general-purpose technique for making sense of neural networks that are trained to perform natural-language-processing tasks, in which computers attempt to interpret freeform texts written in ordinary, or "natural," language (as opposed to a structured language, such as a database-query language). The technique is analogous to one that has been used to analyze neural networks trained to perform computer vision tasks, such as object recognition. Somewhat ironically, to generate test sentences to feed to black-box neural nets, Jaakkola and David Alvarez-Melis, an MIT graduate student in electrical engineering and computer science and first author on the new paper, use a black-box neural net.
A new $240 million center at MIT may help advance the field of artificial intelligence by developing novel devices and materials to power the latest machine-learning algorithms. The project, announced by IBM and MIT today, will research new approaches in deep learning, a technique in AI that has led to big advances in areas such as machine vision and voice recognition. But it will also explore completely new computing devices, materials, and physical phenomena, including efforts to harness quantum computers--exotic but potentially very powerful new machines--to make AI even more capable. And it will study the economic impact of artificial intelligence and automation, a hugely significant issue for society.
Assistants falling for the ploy included Amazon Alexa, Apple's Siri, Google Now, Samsung S Voice, Microsoft Cortana and Huawei HiVoice, as well as some voice control systems used in cars. When a voice assistant hears these sounds, they still recognise them as legitimate commands, even though they are imperceptible to the human ear. The owner's voice had to be surreptitiously recorded for playback as Apple's system recognises the speaker. To secure voice assistants in the future, sounds outside the human voice range could be suppressed or machine learning algorithms could listen out for similar style attacks, Vaidya says.