AI (artificial intelligence) personal assistants like Apple's Siri and Microsoft's Cortana have gained significant popularity in recent years. Those voice assistants, available on a variety of different tech devices, can help with a number of different tasks from driving directions to general questions. In fact, the Pew Research Center recently conducted a study where some experts claimed that AI technology like those assistants will be integrated into nearly every aspect of people's daily lives by 2025. So how many people currently use AI personal assistants? We asked 1,000 respondents what they think about the technology and how it has impacted their lives.

Scott Weiss of Savills Studley, who represented FXDD in the transaction, said through a spokesman that the company was drawn to the readily usable space, not to mention the views it offered. He declined to provide the asking rent in the deal. Rob Lowe of Cushman & Wakefield represented the IPsoft and did not immediately return a request for comment via a spokesman.

From Pandora to Spotify to Uber to ebay, new companies are disrupting the old with a key platform to enable change: a shift out of the world of enterprise data warehousing and towards a new platform based on machine learning, big & fast data and open source technologies. These changes are allowing new companies to get closer to the insights of their customers, to adapt their business to minute changes and to react and make decisions in real time. This engaging talk will bring some ideas that can help organizations shape their big data journey and adapt to the coming machine learning age.

The year 1997 saw the ultimate man versus machine tournament, with chess grandmaster Garry Kasparov losing to a machine called Deep Blue. Earlier this year, in what was hailed as another breakthrough in artificial intelligence (AI) research, Google's AlphaGo defeated a professional Go player. Go is an ancient Chinese board game that has hitherto been difficult for a computer to play at a high level due to its deceptively complex gameplay. Where chess is played on a board of 8 x 8 squares, Go is typically played on a board of 19 x 19 squares. These are all worthy engineering achievements, but what does it mean for research into genuine machine intelligence and the predicted artificial intelligence that will surpass human intelligence?

Do you remember that memorable scene in the Harry Potter books when a person seeking revenge on Ron turns out to be Dumbledore hiding behind a cream cake? If you don't, that's probably because you read the version of Harry Potter written by J.K. Rowling instead of an LSTM recurrent neural network -- trained to generate new Hogwarts-related stories using a data set consisting of the series' first four books. "I've been experimenting with deep learning over the past few weeks, and the Harry Potter story is the result of one of those experiments," creator Max Deutsch tells Digital Trends. "Beyond just looking for a fun way to practice what I've been learning, the Harry Potter project was an attempt to make something enjoyable to read." Since the results are more surreal mash-up than anything likely to give Rowling sleepless nights, "enjoyable" may not be exactly the word.

Well, it looks like HubSpot co-founder and CTO Dharmesh Shah accidentally leaked the chatbot he's been working on for the company -- something he teased on Medium a few weeks ago. It's called GrowthBot, and its website calls it a "chatbot for marketing and sales." The bot is active on Facebook Messenger and through text message, and it lets you perform a number of queries, including "find companies in Boston using HubSpot," "what tech does Drift.com It also has a list of HubSpot-specific commands, showing that it may already integrate with some of HubSpot's software: The bot has commands for Google Analytics ("how is traffic this week?") and Medium ("start medium post"). Shah confirmed to BostInno that he accidentally tweeted a link to GrowthBot's site -- which is where we saw it -- in what was intended to be a private conversation.

The next BriefingsDirect business innovation thought leadership discussion focuses on how new modes of buying and evaluating goods and services are disrupting business procurement. We'll hear now from a leading industry analyst on how machine learning, cloud services, and artificial intelligence-enabled human agents are all combining to change the way that companies can order services, buy goods, and even hire employees and contractors. This business process innovation exchange comes to you in conjunction with the Tradeshift Innovation Day held in New York on June 22, 2016. To learn more about how new trends are driving innovation into invoicing and spend management, please join me in welcoming Pierre Mitchell, Chief Research Officer and Managing Director at Azul Partners, where he leads the Spend Matters Procurement research activities. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

KisanHub, a Cambridge, UK-based agtech startup, raised over 1m in funding. The round was led by Notion Capital with participation from NIAB, an international center for plant research, crop evaluation and agronomy. The company intends to use the funds for R&D, sales and customer delivery. Led by Dr Sachin Shende, CEO, Giles Barker, COO, KisanHub provides a SaaS based farming enterprise software platform which leverages precision agriculture, big data, cloud computing, machine learning and mobile to deliver decision points to over one thousand farmers and multiple large enterprises. The company also has operations in Pune.

I just got a new laptop with a GTX 960 graphics card and I decided to jump on the DNN bandwagon, but just going to Nvidia's page it enumerates more than 10 libraries, some more and some less known. For reference, my background is Python/R and Bayesian ML.

I'm new to machine learning and been learning SciKit Learn and really love it. It is amazingly easy and allows really quick results for someone with no experience. My friend says it is all deep learning and neural networks. How hard is the transition to tensorflow, theano and less if I only know SciKit Learn and about 6-7 algorithms. I understand kfolds, CV and gridsearch. Outside of the simplicity and speed is there any reason to continue learning SciKit Learn and how much will carry over to neural networks and deep learning?