Five Factors That Make Deep Learning Different - Go Deep Baby!

At the highest conceptual level, deep learning is no different from supervised machine learning. Data scientists start with a labeled data set to train a model using an algorithm and, hopefully, end up with a model that is accurate enough at predicting the labels of new data that is run through the model. For example, developers can use Caffe, a popular deep-learning library, to train a model using thousands or millions of labeled images. Once they train the model, developers can use it within applications to probabilistically identify objects in a new image.  Conceptually like machine learning, yes, but deep learning is different because:

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AI Is Not An Exception – Technology Has Always Taken Jobs

Yes, AI will take jobs away from many workers - our relatives, friends, and neighbors. So too have all technologies created throughout human history. We invent things to make things easier and the impossible possible. The invention of the wheel made transport easier. Gutenberg’s printing press put lots of monk’s out of business. The chainsaw saw a reduction in the number of sawyers (lumberjacks). Modern medicine created a sharp decrease in snake oil charlatans. The Wang word processor annihilated typing pools. The list goes on. Technology changes how and who performs work, but it also enables new work that no one ever imagined. AI is but another technology in a long list of technologies dating back to the blunt club.

The culprit is gray matter

It is human intelligence. There is nothing that can stop it. But, it is that same gray matter that finds a way – a way for humanity to flourish – at least statistically. If life is precious, then the last hundred years have seen a dramatic increase in life expectancy. According to the National Institute On Aging, the most dramatic and rapid gains have occurred in East Asia, where life expectancy at birth increased from less than 45 years in 1950 to more than 74 years today.

AI will short-term replace workers just as all technology has, but longer term it will raise wages as human workers become exponentially more productive because their efforts are augmented by intelligent machines – non-human servants.

We can go back or we can go forward. Let’s go forward.

Fourteen Machine Learning Solutions For Data Scientists - Which One Is Best For You?

Yogi Berra, Machine Learning For Predictive ModelsThe Power To Predict Is Mighty

Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect. Predictions about the next best action to take in a business process. Predictions about when an expensive asset is likely to break down. Virtually any customer journey, business process, and even strategic decision can be made better if permeated with the power to predict.

Predictive Analytics And Machine Learning Solutions Make It Possible

Yes, making accurate predictions is tough, but predictive analytics and machine learning (PAML) solutions provide data scientists and developers alike with the tools to make it happen. Forrester defines PAML solutions as:

Software that provides data scientists with 1) tools to build predictive models using statistical and machine learning algorithms and 2) a platform to deploy and manage predictive production models.

The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017

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What Exactly The Heck Are Prescriptive Analytics?

Prescriptive analyticsPrescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique. Forrester formally defines prescriptive analytics as:

"Any combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications."

Prescriptive Analytics Inform And Evolve Decision Logic Whether To Act (not not act) And What Action To Take

Prescriptive analytics can be used in two ways:

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Scifi Version Of AI Is Not Available Yet

Spark Summit East came to Boston this year and I was there to enjoy it including being interviewed by Dave Vallente and George Gilbert about Apache Spark and AI on The Cube. We talk about the waning of the term "Big Data" , but get quickly into the future of AI and Apache Spark.

AI Makers Will Squelch Free Speech

Artificial intelligence (AI) is real, albiet maturing slowly. You experience it when you talk to Alexa, when you see a creepily-targeted online ad, and when Netxflix turns you on toArtificial Intelligence Stranger Things. Oh yea, and that self-driving car over there is AI super-powered! AI is indeed cool, but many are scared about how it ultimatley may impact society. Stephen Hawking, Elon Musk, and even the Woz warned that "...artificial intelligence can potentially be more dangerous than nuclear war." In a nutshell, they are concerned about AI that may evolve to outsmart humans and kill people - a valid concern. But, I have another more terrifying concern that would likely be an insidious precursor to runaway, killer AI.

Billionaires And Tech Giants Will Censor AI

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AI Is The Sincerest Form of Flattery ... And Fear

Pure AI is true intelligence that can mimic or exceed the intelligence of human beings. It is still a long way off, if it can even ever be achieved. But what if AI became pure — could perceive, think, act, and even replicate as we do? Look to humanity for the answer. Humanity has been both beautiful and brutal:

  • The beauty of ingenuity, survival, exploration, art, and kindness.
  • The brutality of crime, war, and pettiness.
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Micro Explanations For Nine Essential AI Technologies

Artificial Intelligence is rampant in the movie Ex MachinaArtificial Intelligence (AI) is not one big, specific technology. Rather, it is comprised of one or more building block technologies. So, to understand AI, you have to understand each of these nine building block technologies. Now, you could argue that there are more technologies than the ones listed here, but any additional technology can fit under one of these building blocks. This is a follow-on to my post Artificial Intelligence: Fact, Fiction, How Enterprises Can Crush It

Here are the nine pragmatic AI technology building blocks that enterprises can leverage now:

■        Knowledge engineering. Knowledge engineering is a process to understand and then represent human knowledge in data structures, semantic models, and heuristics (rules). AD&D pros can embed this engineered knowledge in applications to solve complex problems that are generally associated with human expertise. For example, large insurers have used knowledge engineering to represent and embed the expertise of claims adjusters to automate the adjudication process. IBM Watson Health uses engineered knowledge in combination with a corpus of information that includes over 290 medical journals, textbooks, and drug databases to help oncologists choose the best treatment for their patients.

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Artificial Intelligence: Fact, Fiction. How Enterprises Can Crush It

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models.

But the burning question is: how can your enterprise use AI today to crush it? To answer this question we first must bring clarity to the nebulous definition of AI.Let’s break it down further:

■        “Artificial” is the opposite of organic. Artificial simply means person-made versus occurring naturally in the universe. Computer scientists, engineers, and developers research, design, and create a combination of software, computers, and machine to manifest AI technology.

■        “Intelligence” is in the eye of the beholder.  Philosophers will have job security for a very long time trying to define intelligence precisely. That’s because, intelligence is much tougher to define because we humans routinely assign intelligence to all matter of things including well-trained dachshunds, self-driving cars, and “intelligent” assistants such as Amazon Echo. Intelligence is relative. For AI purists, intelligence is more akin to human abilities. It means the ability to perceive its environment, take actions that satisfy a set of goals, and learn from both successes and failures. Intelligence among humans varies greatly and so too does it vary among AI systems.

Temper Your Expectations, But Don’t Give Up On AI

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On-Premise Hadoop Just Got Easier With These 8 Hadoop-Optimized Systems

Enterprises agree that speedy deployment of big data Hadoop platforms has been critical to their success, especially as use cases expand and proliferate. However, deploying Hadoop systems is often difficult, especially when supporting complex workloads and dealing with hundreds of terabytes or petabytes of data. Architects need a considerable amount of time and effort to install, tune, and optimize Hadoop. Hadoop-optimized systems (aka appliances) make on-premises deployments virtually instant and blazing fast to boot. Unlike generic hardware infrastructure, Hadoop-optimized systems are preconfigured and integrated hardware and software components to deliver optimal performance and support various big data workloads. They also support one or many of the major distros such as Cloudera, Hortonworks, IBM BigInsights, and MapR.  As a result, organizations spend less time installing, tuning, troubleshooting, patching, upgrading, and dealing with integration- and scale-related issues.

Choose From Among 8 Hadoop-Optimized Systems Vendors

Noel Yuhanna and me published Forrester Wave: Big Data Hadoop-Optimized Systems, Q2 2016  where we evaluated 7 of the 8 options in the market. HP Enterprise's solution was not evaluated in this Wave, but Forrester also considers HPE a key player in the market for Hadoop-Optimized Systems along with the 7 vendors we did evaluate in the Wave. 

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