Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world. In contrast to many AI vision systems, the DeepMind program makes sense of a scene more the way a person does.

Artificial Intelligence can now dream an entire world based on a single photo. The intelligent system, developed as part of Google's DeepMind AI program, has taught itself to visualise any angle on a space in a static photograph. Dubbed Generative Query Network, it gives the machine a'human-like imagination'. This allows the algorithm to generate three-dimensional impressions of spaces it has only ever seen in flat, two-dimensional images. The AI breakthrough was announced by DeepMind CEO Demis Hassabis.

Amazon has begun shipping its delayed DeepLens, a $250 AI-powered camera which is designed to put machine learning capabilities into the hands of every software developer. Announced back in November 2017, the camera's full name is the Amazon Web Services DeepLens AI Deep Learning Video Camera. Picking that name apart, it is a camera which hooks up to Amazon's AWS web service and lets developers try their hand at creating apps which use machine learning. Amazon says it is the first video camera designed to teach developers the basics of deep learning, and to do this is comes with six sample projects to get you started. These include object detection, a system for correctly identifying cats and dogs, activity detection and face detection.

E-commerce giant Amazon's cloud services company, Amazon Web Services, has started shipping its $249 camera DeepLens geared for deep-learning tasks, said Jeff Barr, chief evangelist for AWS. Deep learning is a subfield of machine learning, which in turn is a part of artificial intelligence. DeepLens is a video camera that runs deep-learning models, Barr said. The camera can be used in several verticals such as military, healthcare, hospitality and education. In terms of hardware, the DeepLens comes with a four megapixel camera (1080p video), a 2D microphone array, and draws power from an Intel Atom Processor.

In business software, the computer chip has been forgotten. Robotics has been more tightly tied to individual hardware devices, so manufacturing applications are still a bit more focused on hardware. The current state of Artificial Intelligence (AI), in general, and Deep Learning (DL) in specific, is more tightly tying hardware to software than at any time in computers since the 1970s. While my last few "management AI" articles were about overfit and bias, two key risks in a machine learning (ML) system. This column digs deeper to address the question many managers, especially business line managers, might have about the hardware acronyms constantly mentioned in the ML ecosystem: Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). It helps to understand that the GPU is valuable because it accelerates the tensor (math) processing necessary for deep learning applications.

Online search is now the first step for a majority of travelers, with some consumers visiting up to 38 sites before booking a ticket. Yet the travel industry must adapt to newer digital marketing strategies to win over potential customers. The key to success is delivering ultra-precisely targeted content, leveraging personalized retargeting combined with AI and deep learning. A single customer looking to book a trip can visits hundreds of travel pages each day. The search often takes weeks before the final purchase is made.

To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation. The open-source machine learning library, Tensorflow, was used to retrain a final layer of the deep convolutional neural network, Inception, to perform binary normality classification on two, anonymised, public image datasets. Re-training was performed on 47,644 images using commodity hardware, with validation testing on 5,505 previously unseen radiographs. Confusion matrix analysis was performed to derive diagnostic utility metrics. This study demonstrates the application of a machine learning-based approach to classify chest radiographs as normal or abnormal.

Elizabeth Holm, a professor of materials science and engineering at the College of Engineering at Carnegie Mellon University and a computational materials scientist at Sandia National Laboratories says we're in the midst of an artificial intelligence (AI) culture shift. She also says that machines won't replace human experts. "Machines are great at handling things, like large amounts of data, but machines still need an expert, a human, to analyze the data, set parameters and guide decisions," said Holm. "Engineering and science decisions are based on understanding how things work. How does a bridge support its load? How does an engine convert fuel into motion? In contrast, AI transforms data into decisions without understanding any underlying principles," said Holm. "Applying AI to engineering and science will require a culture shift: either we will learn to trust decisions that we do not understand, or AIs will evolve to base their decisions on principles that humans can interpret and control."

We didn't actually mention any of that stuff, but your brain knows that since fridges are usually found in the kitchen, those other appliances are likely nearby. When the mental image comes up short, our brains use such assumptions to fill in the gaps. Our imaginations aren't just for day dreams, they're one of the main ways humans make sense of a world where we're not always given all the information we need or want. It's part of why we humans are able to see, think, and act on what's going on around us. We've learned not to stand on tables and to sit in chairs through observation and interaction, two things that come naturally to us.

Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world.