I took a deep learning course last year, and found it was a pain to write a web app, stand up servers in the cloud, register domain names, etc. So I built something that "webapp-ifies" Keras image recognition models and deploys it to the web. All you need to do is upload a trained Keras model. Things I'll be improving next (I had to start somewhere):

Now that we have such amazing data sets, algorithmic learning is far more effective. CGP Grey also put out a video that describes what's going on inside the algorithm. He uses metaphors, such as a teacher bots and builder bots, to try and explain the machines' process of learning. "In ye olden days, humans built algorithmic bots by giving them instructions the humans could explain: if this, then that," he says. "But many problems are just too big and hard for a human to write simple instructions for." Image recognition is the classic example of a "just too big and hard" problem. How do I explain the concept of a "bee" to a computer so that it can correctly identify one in any given photo?

Now that we have such amazing data sets, algorithmic learning is far more effective. CGP Grey also put out a video that describes what's going on inside the algorithm. He uses metaphors, such as a teacher bots and builder bots, to try and explain the machines' process of learning. "In ye olden days, humans built algorithmic bots by giving them instructions the humans could explain: if this, then that," he says. "But many problems are just too big and hard for a human to write simple instructions for." Image recognition is the classic example of a "just too big and hard" problem. How do I explain the concept of a "bee" to a computer so that it can correctly identify one in any given photo?

This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem. In addition, the approach has a high "speed" of adaptation, i.e. it requires an extremely low number of labeled target training samples, even one per category can be effective. We then extensively compare this approach to the state of the art in domain adaptation in two experiments: one using datasets for handwritten digit recognition, and one using datasets for visual object recognition.

Jeetu Patel, Chief Product Officer at Box, announced the release of the new machine-learning-focused product Box Skills at BoxWorks 2017. To kick off this 25-minute product announcement, Box CEO Aaron Levie talks about the background for Box Skills -- a technology climate heavily influenced by the rise in mobile devices, the power of cloud computing, the unstoppable growth of the internet and the recent focus on machine-learning applications. We're seeing these trends converge in our personal lives in things like virtual personal assistants (Alexa, Siri), but personal applications are just the start for machine learning. Technology in the enterprise is where AI and machine learning will fundamentally change the way we use information in the cloud. Over the past few years, there has been a tremendous amount of innovation (and spending) around machine learning, specifically to solve business-case problems for things like voice recognition and image recognition.

Detecting and reading text from photos has multiple use cases, be it clicking a picture of a printed text and automatically converting it into a digital file or the new age application of reading bills and invoices. Other interesting use cases include deep image search, understanding local business listing using street view images or when combined with text translation the ability to take a picture of a billboard in a foreign country and have it converted to your native language, the possibilities are limitless. Image text recognition is a class of computer vision problems which, among other things, includes OCR (optical character recognition) or text detection (used to find printed text on images) or handwritten text recognition. With the advancement of deep learning we have come a long way to get substantially better at text recognition, but still, the best companies in the business have much to cover before we can consider this problem as solved. Most of the major technology companies/cloud services provide APIs to recognize text in an image.

Rarely does a day go by without more news predicting the end of work. After all, autonomous vehicles are all but certain to replace truckers and taxi drivers in the coming decades, and robots have already taken over many jobs in factories and warehouses, and will continue to expand their reach beyond heavy industry as they become smarter and ever more affordable. Perhaps most frighteningly, even professional services no longer seem safe from the encroachment of increasingly sophisticated artificial intelligence (AI). Law firms, for example, employ electronic-discovery software, which uses natural language processing to sift through reams of documents faster and more cheaply than the entry-level lawyers who used to do this tedious work. Deep-learning image recognition tools can flag and classify worrisome tumors in digital scans as well as, or better than, experienced radiologists.

Big Data, as we all know, can now seen to be streaming into businesses as well as all over the Internet from various data sources may it be sensors, social media data, reviews, customer data, or even more. Many big recognized companies like Google, IBM, Microsoft and Amazon have a hand in helping businesses process big data by building Machine Learning APIs so that they can in return make the best optimum use of the machine learning technology. Very Similar to how developers create applications with the use of standard APIs, Machine Learning APIs for everyone, make machine learning easy to use. These Machine Learning APIs result in the abstraction of the complexities that are further involved in the creation as well as the deployment of machine learning models so that developers can focus even more on user experience, design, experimenting as well as delivering insights from data. Application developers always find ways to look for various ways to ease the lives of their users by the introduction of features that are novel and innovative that can help users save time.

This blog post discusses how to turn your images into text describing what is in them so you can later perform analysis on their contents and topics, all right out of a Jupyter Notebook. An example of when this would be useful is if you are given thousands of tweets, and want to know if the image media has any effect on engagement. Lucky for us, instead of writing our own image recognition tool, the engineers at Amazon, Google, and Microsoft completed this task and made their APIs accessible. Here we'll be using Rekognition, Amazon's deep learning-based image and video analysis tool. This blog serves as an example for how to extract information using different Rekognition operations and is not a replacement for reading the documentation.

There was an interesting discussion initiated by Peter Schoof at BPM.com: So? How big of an impact do you think artificial intelligence will have on processes in 2018? Artificial intelligence is a multi-dimensional subject area. It can be broadly classified as a blend of machine learning, predictive/adaptive analytics, NLP, text analytics, voice pattern recognition, image analytics, deep learning, graph analytics, robotics, and much more. The current state of adoption of AI complementing business processes in an enterprise is a bit chaotic with discussions, debates, PoCs, R&D, etc. Hopefully, the coming year will mark a more structured and focused approach. AI implementation is not new for the enterprise in terms of processes -- they are already using AI in one form or another (magnitudes may differ).