AI is one of the hottest trends in tech right now, and therefore it’s no surprise that there is literally an army of consulting companies out there right now who are ready willing and able to help clients pursue the dream of AI. However, with a saturated market comes the age old question of “do these guys really know what they are doing?” In a field like AI where the technology itself is often too complex to comprehend it can be very easy to partner with a company that says all the right things but doesn’t have the chops to back any of it up.
I recently read an article on Forbes that sought to help consumers weed out the charlatans by listing the top 5 questions to ask to ensure that an AI vendor can deliver results. Being an AI…
Whether you call them conversational agents, dialog systems, or chatbots, AI-powered bots that can hold human-like conversations are seeping into our everyday lives.
Chatbots work well in a structured environment with a predetermined dataset. Answering simple questions, for example, would be a task a chatbot could excel at. Which is why chatbots are now replacing the Frequently Asked Questions page on websites.
Here’s what you need to know about how these chatbots work and why you might never see a traditional FAQ page again:
Chatbots can be either retrieval-based or generative, which means they can either retrieve data from a predetermined dataset or generate new responses from scratch. These bots can also be open or closed domain, depending on whether the user can take the conversation anywhere and still expect a reply or whether a user needs to stick to a narrow…
From left, Rangan Majumder, Yi-Min Wang and Jianfeng Gao on Microsoft’s Redmond, Washington, campus. Photo by Dan DeLong.
Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation.
Now, the company’s leading AI experts are working on systems that can do something even more complex: Read passages of text and answer questions about them.
“We’re trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it’s written or orally,” said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year.
The Maluuba team is one of several groups at Microsoft that are tackling the challenge of machine reading. Two other research teams, one at the company’s Redmond, Washington, headquarters and the…
Microsoft’s vision for AI (artificial intelligence) is about people. It’s about amplifying human ingenuity through intelligent technology that will reason with, understand and interact with people and, together with people, help us solve some of society’s most fundamental challenges. This was the message shared on July 12 at an event in London by Harry Shum, Executive Vice-President of Microsoft’s AI and Research Group.
The event was attended by scientists, technology experts and journalists, who gathered to learn more about Microsoft’s AI intentions from Shum and other Microsoft executives, including Eric Horvitz, Technical Fellow and Director at Microsoft Research Labs, Chris Bishop, Technical Fellow and Laboratory Director at Microsoft Research Cambridge, and Emma Williams, General Manager at Bing.
During the discussion, a number of announcements were made that further reinforced Microsoft’s focus on AI, including a new program that will make technology available to those working to…
For reliability, accuracy and performance, both AI and machine learning heavily rely on large sets. Because the larger the pool of data, the better you can train the models. That’s why it’s critical for big data platforms to efficiently work with different data streams and systems, regardless of the structure of the data (or lack thereof), data velocity or volume.
However, that’s easier said than done.
Today every big data platform faces these systemic challenges:
Compute / Storage Overlap: Traditionally, compute and storage were never delineated. As data volumes grew, you had to invest in compute as well as storage.
Non-Uniform Access of Data: Over the years, too much dependency on business operations and applications have led companies to acquire, ingest and store data in different physical systems like file systems, databases and data warehouses (e.g. SQL Server or Oracle), big data systems (e.g….