Sorites de produit, innovation, start-up
It was great speaking with Michael Berthold, Founder and CEO at KNIME during their fall summit. Michael created KNIME after seeing all of the great data pharmaceutical companies were generating but also seeing the difficulty they had garnering insights due to the challenges of massaging and analyzing the data.
KNIME is an open platform that enables organizations to put their data to good use. Open data science platforms enable:
Despite objections from employees, law enforcement officers, and the ACLU, Amazon Web Services announced last Thursday that it would continue to sell facial recognition software, the AWS Rekognition system.
In an all-hands meeting on Thursday, AWS CEO, Andrew Jassy, explained their reasons for continuing to sell Rekognition to law enforcement, saying, "Rekognition is actively been used to help stop human trafficking, to reunite missing kids with parents for educational applications, for security and multi-factor authentication to prevent theft."
"One of the most surprising things I’ve seen with Neo4j is the speed at which we’re able to innovate and deliver features to our customers," said Mark Hashimoto, Senior Director of Engineering, Digital Home at Comcast.
In this week’s five-minute interview, we discuss how Comcast uses the flexibility of the graph data model to develop and launch new features rapidly using Neo4j for persistence.
It’s well known that user reviews are a fundamental part of the web economy, with consumers tending to trust the word of their fellow customer above any other form of marketing. Making sure those reviews are truthful and authentic therefore is key, especially as unscrupulous vendors are happy to produce fake reviews to puff up their service. A recent study from Aalto University highlighted how it’s increasingly possible to create fake reviews autonomously.
The authors state that around 40% of us make a decision based upon the feedback received from other people, whilst a good review can boost sales by around 30%. In other words, they’re important to the success of a product, which creates an incentive to create fake reviews to boost your ratings. Are AI-driven technologies increasingly capable of producing accurate reviews?
Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work. It’s a great opportunity for programmers who are willing to learn these new skills and upgrade themselves. It’s also important from the job perspective because Robots and Bots are getting smarter day by day, thanks to these technologies and most likely will take over some of the jobs which many programmers do today. Hence, it’s important for software engineers and developers to upgrade themselves with these skills. Programmers with these skills are also commanding significantly higher salaries as data science is revolutionizing the world around us. Machine Learning specialist is one of the top paid technical jobs in the world. However, most developers and IT professionals are yet to learn these valuable set of skills.
For those, who don’t know what is a Data Science, Machine Learning, or Deep Learning, they are very related terms with all pointing towards machine doing jobs which is only possible for humans till date and analyzing the huge set of data collected by modern day application.
Technology can be a blessing in disguise. It is especially true in the modern classroom. While the Internet and a host of multimedia tech do make it easier to perform various tasks and deliver educational content, there are always concerns about its overall effectiveness in learning.
In a world where most interactions either begin or, at a minimum, include the use of screens and online content, teachers and students have two choices: avoid the tech and leave it out of the process or embrace it and develop ways to use it to the student’s advantage.
Optical Character Recognition (OCR) tools have come a long way since their introduction in the early 1990s. The ability of OCR software to convert different types of documents such as PDFs, files, or images into editable and easily storable format has made corporate tasks effortless. Not only this, it’s ability to decipher a variety of languages and symbols gives Infrrd OCR Scanner an edge over ordinary scanners.
However, building a technology like this isn’t a cakewalk. It requires an understanding of machine learning and computer vision algorithms. The main challenge one can face is identifying each character and word. So in order to tackle this problem we’re listing some of the steps through which building an OCR scanner will become much more clearer. Here we go:
I’ve written a number of times about the need to significantly expand our efforts to educate the workforce if we’re to take advantage of the latest technologies entering the market. Consulting firm Accenture has been fervent cheerleaders of this, and have banged the drum again in a recently published paper.
They argue that unless we take a radically new approach to learning, the skills gap will result in as much as $11.5 trillion in GDP growth being lost over the next ten years.
While Artificial Intelligence and Machine Learning provide ample possibilities for businesses to improve their operations and maximize their revenues, there is no such thing as a “free lunch.”
The “no free lunch” problem is the AI/ML industry adaptation of the age-old “no one-size-fits-all” problem. The array of problems the businesses face is huge, and the variety of ML models used to solve these problems is quite wide, as some algorithms are better at dealing with certain types of problems than the others. Thus said, one needs a clear understanding of what every type of ML models is good for, and today we list 10 most popular AI algorithms:
In preparation for my talk at the Philadelphia Open Source Conference, Apache Deep Learning 201, I wanted to have some good images for running various Apache MXNet Deep Learning Algorithms for Computer Vision.
Using Apache open source tools – Apache NiFi 1.8 and Apache MXNet 1.3 with GluonCV I can easily ingest live traffic camera images and run Object Detection, Semantic Segmentation and Instance Segmentation.
