WHY IT MATTERS: Digital Transformation

The era of easily-faked photos is quickly emerging—much as it did when Photoshop became widely prevalent—so it’s a good time to remember we shouldn’t trust everything we see.

Google cloud vision API allows developers to analyze the content of images.

For the CIA, hedge funds and the largest retail enterprises, the confounding problem is the same: too much data. The world and its actors have never seemed more complex, and with no way to absorb a meaningful part of the information out there, events appear harder than ever to understand.

A business opportunity: We are seeing a slew of startups promoting artificial intelligence as a solution. The latest is Primer, which, backed by $14.7 million in venture funding, says it sorts millions of news sources and any other data thrown at it, and then crystallizes what's important in concise, natural language.

Find out how you can make use of Google's machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide access to pre-trained machine learning models with a single API call: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models. In this session we'll share an overview of each API and dive into code with some live demos.

The AlphaGo Zero agent destroyed the original AlphaGo that beat Lee Sedol 100-0 after just three days of training.

To give a car the ability to Sense, Plan & Act (SPA) requires a complex system of hardware and software, all of which works together in (hopeful) harmony to form a self-driving car.

The start-up Voyage is testing its self-driving taxi service in a gated community of about 4,000 residents where the average age is 76. There's another benefit to testing in a retirement community: It's private property. That means Voyage doesn't have to share ride information with state regulators, freeing it from some bureaucracy. But testing in the community meant different obstacles, like insurers requiring Voyage to have double California's $5 million in coverage funds and to hand over all driving data. To reassure the retirement community, Voyage gave them as much equity as they give to a new hire. That aside, retirees have a lot to gain from self-driving cars. Losing the ability to drive often cuts folks off from the outside world, so it's interesting to see Voyage explore where other self-driving leaders haven't been yet.

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

Explore TensorFlow Playground demos. See how they explain the mechanism and power of neural networks, which extract hidden insights and complex patterns.

Majority of websites are generic, one-size-fits-all, and unvarying. And email isn’t much better with manual input, static cadences, and fixed content. Marketers and audiences deserve better, though. As big data, artificial intelligence (AI), machine learning, and deep learning become larger influences and more attainable, now is the time for digital marketers to seize upon the potential these technologies offer. Brands can now build custom on-site and email experiences for individuals that are automated and personalized by incorporating predictive modeling, journey mapping, and algorithms.

Instacart is saving minutes per delivery by sorting shopping lists using deep learning. Emojis help to define the problem and outline both a simple and a more complex deep learning architecture.

Businesses everywhere are in the process of embracing digital transformation. However, many are looking to the future and the continuing changes it may bring to their industries. Forrester analysts and business representatives from all manner of industries provided insight into continuing digital business developments and what they may mean for every company in the years to come.

Automation is happening, and it will bring substantial benefits to businesses and economies worldwide, but it won’t arrive overnight. A new McKinsey Global Institute report finds realizing automation’s full potential requires people and technology to work hand in hand.

The legal profession relies more and more on automation. But fears that it will be automated out of existence are overblown, researchers say. For now.

Teachable Machine, an experiment that makes it easier for anyone to explore machine learning. Teach a machine using your camera – live in the browser, no coding required.

The Hype Cycle for Emerging Technologies provides insights gained from evaluating more than 2,000 technologies. The eight added in 2017 include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing.

Forrester says that CIOs must act as VCs and recommends they support early-stage tech companies and have their own technology scouts. As CIO Angela Yochem told me in 2015, she has set up a process to find “capabilities that are not yet commercially available” and “quickly absorb, test, and utilize emerging technologies, and discard them if they are not appropriate and don’t give us a leg up on our competition.”

Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. AI investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.

If you’re a designer or engineer in the building, infrastructure, or manufacturing industries, you have probably heard about generative design, and maybe you’re excited—or skeptical. Or maybe you’re wondering, what is generative design? Will it pave the road for a new future of making things? Or will artificial intelligence usurp entire governments to become the overlords of 

What if a CAD system could generate thousands of design options that all meet your specified goals? It’s no longer what if: it’s Project Dreamcatcher, the next generation of CAD. Dreamcatcher is a generative design system that enables designers to craft a definition of their design problem through goals and constraints. This information is used to synthesize alternative design solutions that meet the objectives. Designers are able to explore trade-offs between many alternative approaches and select design solutions for manufacture.

I’ve been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn’t been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence (AI). Now is the time to rethink the modern role of the designer.

The AI Race - Documentary ABC TV The world of the future and how jobs will be impacted with artificial intelligence

The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years. Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems.