Most ZDNet readers know enough about digital photography - and video - to know that enlarging a digital picture means the pixels get bigger too. Blowing up a fuzzy surveillance video just makes a large fuzzy video. It's why optical zoom is superior to digital zoom in a camera. However, there is a lot of work going on in the field of image super resolution (ISR), and they've produced some amazing results. The problem is, of course, that you don't know what the image under those pixels looks like.

This notebook was written by Dr. Randal S. Olson from GitHub. In this notebook, Randal is going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like. In addition to providing code examples, he also hopes to imbue in you a sense of good practices so you can be a more effective -- and more collaborative -- data scientist. Randal will be following along with the data analysis checklist from The Elements of Data Analytic Style, which he strongly recommends reading as a free and quick guidebook to performing outstanding data analysis. In the time it took you to read this sentence, terabytes of data have been collectively generated across the world -- more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields -- statistics, physics, computer science, design, and many more -- the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.

Big Data is becoming too big to manage manually. The amount of data coming from sensors, streams and social media is astronomical--but that's only part of the problem. Out of all the data that is being collected, only a small amount of it is actually essential, making it an impossible task to find the needle (value) in the haystack (data). "Data collection is easy," said Sri Ambati, CEO of H2O.ai, a machine learning solution provider. "But it is not just about collecting data for your customer anymore; it is knowing what they want that makes a big difference." In order to sift out the value from all the data, organizations are turning to machine learning technologies to learn from their data, make sense of their data, and make better business decisions based on the data. "Machine learning is the crucial link between business use, between applications at the business level, and between ROI to the actual collection of data," said Ambati. Big Data has become the norm in today's enterprise, and machine learning is now becoming imperative to that norm, according to Steven Noels, cofounder and CTO of NGDATA, a Big Data analytics and management provider. Businesses need to continuously pull insights out of their massive amounts of data in order to improve customer experience, streamline business processes, optimize solutions, and understand the business in real time.

In Munich, Germany, the technology conglomerate Siemens AG is betting $1.1 billion on digital technologies, such as the proposition that blockchain data can be leveraged by machine learning to improve the secure transmission of data used in energy trading. Siemens is welcoming its employees and independent firms to bid for the money if they are willing to research how Siemens can develop businesses that use artificial intelligence. Siemens is just one of a number of companies embracing machine learning--the combination of artificial intelligence and deep analytics that enables enterprises to make predictions on large amounts of data and allows developers to experiment by incorporating features like speech and pattern recognition, as well as statistical techniques, into their analysis. And HPE's recent announcement of "machine learning as a service" (MLaaS) is aimed at helping the process really take off. Speaking at HPE Discover Las Vegas 2016, HPE Executive Vice President Robert Youngjohns called machine learning and deep analytics the most fundamental change we're ever going to see.

Over the course of the last 12 years or so, we've seen an evolution from large traditional VC firms investing $5-10M per company in the first round of financing to the emergence of "micro" VC firms investing in rounds $1M-$3M dubbed "seed rounds". This evolution has also spawned even smaller firms investing in rounds of several hundreds of thousands of dollars as well in a stage referred to as "pre-seed". As Mark Suster wrote in his post linked above, the emergence of open source software and cloud computing completely eviscerated the costs and barriers to starting a company, leading to deflationary economics where one or two people could start their company without the large upfront costs that were historically the hallmark of the VC industry. These lower barriers to entry has led to a "cambrian explosion" of startups but hasn't necessarily changed the rules of business. Without a defensible moat, it's just about impossible to create a large company with sustainable profits.

With a human voice and mindful mannerisms, she's the first alarm clock you'll be happy to wake up to. A.I. algorithms ease your morning routine by learning about you & what you love. The Bonjour Smart Alarm Clock is like the personal assistant you never had. She's always there early to wake you up and make the most of your day. Bonjour can adjust your wake-up time if certain conditions are fulfilled.

Slack Technologies Inc., a business messaging provider, is the latest company to partner with International Business Machines Corp. IBM 0.62 % to add artificial intelligence to its service. Slack plans to improve Slackbot, its customer-service bot, using IBM's Watson, a collection of artificial-intelligence software delivered as cloud-computing services, the two companies said on Wednesday. The messaging company will use Watson Conversation, an IBM service that processes natural language, to enhance the accuracy and efficiency of the bot, which helps Slack users troubleshoot problems. These and other enhancements will be available to users early next year, Slack said.

"If you don't innovate fast, disrupt your industry, disrupt yourself, you will be left behind." To orchestrate this transformation, organizations must revamp their IT strategies and roadmaps and ingest the value of artificial intelligence and enterprise resource planning (ERP) integration. These technologies go hand in hand because they cover the same spectrum. AI-enabled ERP solutions will by default impact the heart and soul of day-to-day operations. The mix of people, process and technology is going to change.

General Motors has partnered with IBM to add the latter's artificial intelligence smarts to its cars. IBM's Watson will be used to augment GM's OnStar service, which currently offers features like vehicle tracking and turn-by-turn navigation for a monthly subscription fee. The upgraded OnStar Go, though, seems to be more about advertising than anything else. GM says the main use will be to let drivers "connect and interact with their favorite brands," with Watson crunching data on users habits to deliver personalized services. Depending on your outlook, some of these services could be genuinely useful.

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