Here’s an interesting write up on Deep Learning from Forbes, that provides an overview of the technology for non-practitioners.

“During training, you define the number of neurons and layers your neural network will be comprised of and expose it to labeled training data,” said Brian Cha, who is a Product Manager and Deep Learning evangelist at FLIR Systems. “With this data, the neural network learns on its own what is ‘good’ or ‘bad.’ For example, if you want the neural network to grade fruits, you would show it images of fruits labeled ‘Grade A,’ ‘Grade B,’ ‘Grade C,’ and so on.

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on. You don’t need to manually define these characteristics or even program what is too big or too small, the neural network trains itself using the training data. The process of evaluating new images using a neural network to make decisions on is called inference. When you present the trained neural network with a new image, it will provide an inference, such as ‘Grade A with 95% confidence.’”

Computer have wide applications across industries for quality control.

For instance, the majority of all medical data is image-based: The assessment of X-rays and scans is crucial for the right diagnosis and, thus, for the right treatment.

Public health depends on the accurate interpretation of every single image, and many physicians are obliged to choose between longer working hours or doing less detailed and precise medical image analysis.

Right now, medical staffers around the world are stretched thin, this is where AI can come into play.

Artificial intelligence in healthcare speeds up the process of medical image analysis and makes it more accurate and stress-free for medical personnel. Using artificial intelligence, it is possible to detect rare diseases, such as Noonan syndrome, or identify viruses and bacteria by analyzing Petri dish images. Computer vision and machine learning for medical image analysis are becoming as vital as an experienced lab worker with modern equipment.

At first glance, it may not be obvious how reliant Uber is on data or how much of a powerhouse in machine learning and data science that they’ve become. Forbes has an article on their best practices for machine learning model management — a skill every organization needs (or will need) to master.

Uber is one of those organizations that rely heavily on data. Each day, millions of trips take place in 700 cities across the world, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables Uber to deliver a smooth riding experience to its […]

Here’s an interesting look at how far enterprises are planning to go with AI.

62% of organizations are using automation to eliminate transactional work and replace repetitive tasks, 47% are also augmenting existing work practices to improve productivity, and 36% are “reimagining work;” 84% said that automation would require reskilling and reported that they are increasing funding for reskilling and retraining, with 18% characterizing this investment as “significant;” In 10 years, 20-30% of jobs will be ‘superjobs,’ 10-20% will be low-wage, low-skill jobs, and the middle 60-70% will be ‘hybrid jobs’ that require both technical and soft skills;

Here’s an interesting article in Forbes on how computer vision is being applied in the real world.

Even though early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s, today the applications for computer vision have grown exponentially. By 2022, the computer vision and hardware market is expected to […]

Forbes points out that the term “Big Data” has been eclipsed by “Data Science” in the hype cycle. However, the Great Hype Cycle resembles Game of Thrones and I think we can all agree that “AI” or “Machine Learning” is next to sit on the Iron Throne of Hype.

In a world in which “big data” and “data science” seem to adorn every technology-related news article and social media post, have the terms finally reached public interest saturation? As the use of large amounts of data has become mainstream, is the role of “data science” replacing the hype of “big data?”

If you think that the excitement around AI lacks merit, then you may want to reconsider that point of view after reading this article from Forbes.

AI and machine learning are breathing new life and business opportunities into that tired old phrase, “automating paper-based processes.” Consider this trio of forecasts that IDC predicted to happen by 2022: Sixty percent of G2000 enterprises will be AI-enabled AI will help over 50 percent of enterprise application workflows […]