The above graph summarizes the number of Artificial Intelligence companies founded in a certain year. We are currently tracking 1503 Artificial Intelligence companies in 13 categories across 73 countries, with a total of $9.3 Billion in funding. Click here to learn more about the full Artificial Intelligence landscape report and database.

Move over Artificial Intelligence, 'cognitive technology' is the future Artificial Intelligence Program Writes A Christmas Carol With Moments Of Cheer And Darkness ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?

Interesting article posted recently in MIT Technology Reviews. What kind of metrics would help detect such tweets? Whether a picture or not is associated with the tweet Whether a link or not is associated with the tweet Number of typos for the tweet in question, compared with average for the user in question Frequency of tweets (sudden spike) for user in question Keywords (and mi-spelled keywords) typically found in such tweets across drunk users Replies / re-tweets from other twitters (volume, do they contain specific keywords?) Replies / re-tweets from other twitters (volume, do they contain specific keywords?) Which algorithm would you use?

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference? There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning.

Imagine you are into the agricultural sector and want to better the prospects of your farming practices. What if an app in your smartphone could predict the future conditions of weather and market, availability of amount of irrigation water, and at the same time suggest which particular variety of crop will give maximum production in a given land or soil condition? Or let's assume the administrative managers of a city want to have an integrated view of the region's overall performance on the priority areas relevant to the city. This could be grievance management (for instance, in a crisis situation like last year's Chennai floods), operational or financial performance. What if an'intelligent operations centre' allowed them to explore key priority areas in depth and take actions from the solutions provided?

As part of my Technology and Innovation MBA program at Ted Rogers School of Management, I took a data and knowledge management course which teaches students the principles and practices of knowledge management. The second part of the course delves on tools used in data management and analytics. Although the theoretical part of the course was a bit dry, the hands-on portion was very interesting and exposed students to several different tools to capture, clean and analyze data. One of the tasks given to students was to capture and analyze twitter data. Although students had access to Netlytics, which is a neat cloud-based text and social network analysis tool that also collects Twitter data, students were encouraged to find other ways to collect Twitter data.

Earlier this month, the internet got in a froth about Apple's decision to drop the 3.5mm analogue audio jack from the new iPhone. Users took to Twitter to vent their outrage, while tech analysts, such as Paul Erickson at IHS Technology, suggested that the removal was money-driven: "It should be noted that wireless models are the highest revenue-generating products within the headphone market," he told the Financial Times. Further disapproval was directed at Apple's replacement for wired earphones, the AirPod, essentially a wireless earphone and microphone – "like a tampon without a string" according to the Guardian – while the writers of US late-night talkshow Conan created a satirical Apple ad featuring the devices plopping from users' ears to floor and being eaten by their pet dogs. Yet, as Chris Saad, head of product at Uber, has pointed out in a post on Medium, Apple did more than launch some earbuds: "They launched a wireless microphone as well." By which he means the day when we converse all day long with a virtual assistant similar to the one voiced by Scarlett Johansson in Her is drawing closer.

DeepMind cofounder Mustafa Suleyman once again refused to say who sits on Google's mysterious AI ethics board on Monday despite having previously said he wants to disclose it to the public. The board was quietly created in 2014 when Google acquired the London artificial intelligence lab. It was established in a bid to ensure that the self-thinking software DeepMind and Google is developing remains safe and of benefit to humanity. Speaking at the TechCrunch Disrupt conference, Suleyman said: "So look, I've said many times, we want to be as innovative and progressive and open with our governance as we are with our technology. "It's no good for us to just be technologists in a vacuum independently of the social and political consequences, build technologies that we think may or may not be useful, while we throw them over the wall.

Artificial Intelligence Program Writes A Christmas Carol With Moments Of Cheer And Darkness ... Surti flyers to protest against AI's airfares Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.