... includes all of the major AI methods for (a) representing knowledge about a task or a problem area, and (b) reasoning about a problem.
In today's business era, AI chatbots are redefining the way pharma companies interact and engage with their clients. These chatbots mimic human conversation via text or auditory means which is a huge opportunity for the pharma industry to have a one-to-one conversation with their customers, doctors, and patients. Apart from that, by using intelligent virtual assistants, pharmaceutical companies can build a strong relationship with doctors and patients by communicating with them and assisting them directly. The two main areas within this industry that will drastically benefit from developing a pharma chatbot are R&D and marketing. By developing a chatbot, a pharma company can have a virtual digital assistant to provide information to users on various topics, such as how to respond to inquiries on certain health conditions, a complex drug procedure, and the appropriate method of using a certain medical device.
Most product-development tasks are complex optimization problems. Design teams approach them iteratively, refining an initial best guess through rounds of engineering analysis, interpretation, and refinement. But each such iteration takes time and money, and teams may achieve only a handful of iterations within the development timeline. Because teams rarely have the opportunity to explore alternative solutions that depart significantly from their base-case assumptions, too often the final design is suboptimal. Today's technology offers an alternative.
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs is well developed, structure learning leaves something to be desired: even though there is a plethora of SPN structure learners, most of them are somewhat ad-hoc, and based on intuition rather than a clear learning principle. In this paper, we introduce a well-principled Bayesian framework for SPN structure learning. The first is rather unproblematic and akin to neural network architecture validation. The second characterizes the effective structure of the SPN and needs to respect the usual structural constraints in SPN, i.e., completeness and decomposability.
Smart speakers, like Amazon's Alexa and Apple's Siri, have come under fire over the past few years for'listening' to its owner's conversations. Now, a team of scientists believe they have developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone. Dubbed the'bracelet of silence', the chunky bracelet is fitted with 23 speakers around it that emit ultrasonic signals that drown out any speech of the wearer. While these ultrasonic signals are undetectable to human ears, they leak into the audible spectrum after being captured by the microphones, producing a jamming signal inside the microphone circuit disrupts voice recordings. Scientists developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone.
A former Amazon Executive revealed he switches off his Alexa smart speaker whenever he wants a'private moment' as he doesn't want it listening in. Robert Frederick, a former manager at Amazon Web Services, told BBC Panorama he always turns it off during personal and particularly sensitive conversations. Last year Amazon was forced to admit that some conversations recorded by virtual assistant Alexa were listened to and transcribed by humans. Amazon says human staff listen to less than on per cent of conversations to check for accuracy and the information is made anonymous before they see it. Amazon's Alexa is being placed in an increasing number of devices including televisions, smart speakers and screens The investigative journalism programme is exploring Amazon's rise from online bookstore to tech giant as well as the way it collects data from its customers.
Tata Consultancy Services' Capital Markets Focussed Workflow, Innovative Process Enhancers, and Solutions Backed by the Latest Technologies, Cited as Key Strengths Tata Consultancy Services (TCS), a leading global IT services, consulting and business solutions organization, has been recognized as a Leader in the Everest Group PEAK Matrix for Capital Markets Operations. In an assessment of 24 global service providers offering capital markets operations services, TCS was placed highest for Vision and Capability, as well as Market Impact. Additionally, it was named a Star Performer for having top quartile year-on-year improvement in its scores. TCS' strong position in the overall capital markets segment is attributed to consistent growth in its portfolio with multiple new wins. According to the report, the company has continuously worked on creating solutions backed with the latest technology to help its customers solve operational problems more efficiently.
On Tinder, an opening line can go south pretty quickly. And while there are plenty of Instagram accounts dedicated to exposing these "Tinder nightmares," when the company looked at its numbers, it found that users reported only a fraction of behavior that violated its community standards. Now, Tinder is turning to artificial intelligence to help people dealing with grossness in the DMs. The popular online dating app will use machine learning to automatically screen for potentially offensive messages. If a message gets flagged in the system, Tinder will ask its recipient: "Does this bother you?"
Managers and technical personnel unfamiliar with AI are initially dubious about being able to develop even a limited-capability prototype in the space of just one or two months. However, many rapid prototypes have been built in only a few weeks. These prototypes are necessarily limited in the degree to which they meet the overall requirements of the final system. Rapid prototypes provide a number of significant benefits. The feasibility of the project plan can be verified before spending a major part of the budget.
Ten minutes into my date with Taylor and I've spilled the champagne, smashed four glasses and slapped his cheek with uncooked lobster. The table is a blazing inferno (my attempt to light our votive candle was suboptimal). While I fumble with the fire extinguisher, Taylor stares at his phone, arms folded, and purses his lips. Moments like these transform Table Manners from a physics-based dating simulator into something approaching hilarious Mr Bean fan fiction. Table Manners borrows the idea Bossa popularised with Surgeon Simulator by challenging players to complete a series of dexterous tasks via purposefully janky controls.
Facing a fridge full of ingredients but still don't know what to cook? Tired of following the same recipes and eager to try something new and creative? Thanks to AI technologies such as image recognition and machine learning, people can now save time, food and money in the kitchen while discovering creative and tasty recipes and even generating their own new and personalized flavours. Facebook has developed an image-to-recipe generation system which enables users to reverse engineer a recipe by simply inputting an image of the dish they want to prepare. First, ingredients and ingredient co-occurrence are generated by exploiting visual features extracted from the food image.