Humans learn to speak before they can read or write, so why can't computers do the same? In this paper, we present a deep neural network model capable of rudimentary spoken language acquisition using untranscribed audio training data, whose only supervision comes in the form of contextually relevant visual images. We describe the collection of our data comprised of over 120,000 spoken audio captions for the Places image dataset and evaluate our model on an image search and annotation task. We also provide some visualizations which suggest that our model is learning to recognize meaningful words within the caption spectrograms.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation.

Artificial intelligence is a concept that includes automatic or machine learning, so a first approximation to both terms places us already in a context of subordination that in no way implies inferiority. Despite their specificities, both are artificial intelligence systems, and as such they pursue a single purpose: the creation of devices or algorithms that omit or replace human being by emulating their cognitive functions. In this post we will see the approach and key applications that machine learning contributes as a distinctive element, against a general context of artificial intelligence that includes this and other areas. Artificial intelligence research, indeed, focuses on many different fields, among them machine learning or, for example, deep learning, a new area of investigation of this one. In addition, recent years, it has advanced in a surprising way, acquiring a protagonism that seems to endow it with a fictitious autonomy.

Whether you are a newcomer to machine learning, a newbie to specific algorithms or concepts, or a seasoned ML vet looking for a once-over of an algorithm you haven't seen or used in a while, these short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept. Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality. A wide array of clustering techniques are in use today.

In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and discuss our views on reproducibility.

More Open AI and Machine Learning Toolsets Arrive by - Dec. 02, 2016 Google's Open Embedded Projector is a Cool Data Visualization Tool Google Collects Open Artificial Intelligence Demos, Invites You to Contribute The Renaissance Continues for Open Source Artificial Intelligence Microsoft Open Sources Transformative Speech Recognition Toolkit Google Open Sources Powerful Image Recognition Tool Recently, in an article for TechCrunch, Spark Capital's John Melas-Kyriazi weighed in on how startups can leverage artificial intelligence and machine learning to advance their businesses or even give birth to brand new ones. As a corollary avenue on that topic, it's worth noting that some very powerful artificial intelligence and machine learning engines have recently been open sourced. Quite a few of them have been tested and hardened at Google, Facebook, Microsoft and other companies, and some of them may represent business opportunities. Just recently, two new open source entries on this front have emerged, and they are worth investigating. Health Catalyst has created healthcare.ai as a repository of healthcare-focused open source machine learning software, with an eye toward encouraging the healthcare industry to tap into the power of AI and machine learning.

Earlier in December, Apple announced that it would begin allowing its artificial intelligence and machine learning researchers to publish and share their work in papers, slightly pulling back the curtain on the company's famously secretive creation processes. Now, just a few weeks later, the first of those papers has been published, focusing on Apple's work in the intelligent image recognition field. Titled "Learning from Simulated and Unsupervised Images through Adversarial Training," the paper describes a program that can intelligently decipher and understand digital images in a setting similar to the "Siri Intelligence" and facial recognition features introduced in Photos in iOS 10, but more advanced. In the research, Apple notes the downsides and upsides of using real images compared with that of "synthetic," or computer images. Annotations must be added to real images, an "expensive and time-consuming task" that requires a human workforce to individually label objects in a picture.

A computer's ability to accurately identify images is a white whale for many technology companies, from Baidu to Google. One Australian startup has found a corner of the market to dominate, winning contracts with the European Union Intellectual Property Office (EUIPO) and IP Australia for algorithms that can detect and compare logos. SEE ALSO: Airbnb is getting into the airline booking disruption game with'Flights' TrademarkVision, which has support from Australia's CEA Startup Fund, uses machine learning to support image searches that can identify similar trademarks. Having a unique trademark or logo is vital, but many intellectual property registration bodies often require outdated forms of non-visual search that make comparison difficult. Australia, for example, relies on keywords, Europe on Vienna codes and the U.S. on design codes.

Healthcare analytics company Pulse8 is offering a tool to identify and code patient conditions by accessing content from their clinical data and converting it to XML schema for integration with a variety of systems. Called Popul8, the software leverages machine learning, natural language processing as well as optical character and pattern recognition technologies to create what the company described as a data-driven view of healthcare processes. "The goal is to reduce waste, eliminate unnecessary interventions, and improve patient and provider visibility by easily extracting clinical information from both structured and unstructured data," Pulse8 CEO John Criswell said. Popul8 parses and processes XML schema with a 2-stage coding engine. The first stage uses an ICD parser to discover conditions present in or implied by the chart and physician notes.

Stock photo company Shutterstock introduced reverse image search for desktop earlier this spring. This made it easy for users to search Shutterstock's website with an image, instead of using keywords. Shutterstock's data scientist Kevin Lester, who looks closely at the adoption of these new tools, was able to find out what patterns emerge from the data. In fact, Lester shared with IBTimes, that those who used reverse-image search for searches wound up making more downloads per search than those from a user with a text-based search. "We've found that users who performed at least one reverse image search prior to making a purchase with Shutterstock were 3.49 times more likely to make a subsequent purchase than those who did not," says Lester.