Category Archives: Machine Learning

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Would you believe me if I told you artificial intelligence (AI) wrote this article?

With 2020 on the horizon, and with all the progress made in AI and machine learning (ML) already, it probably wouldnt surprise you if that were indeed the case which is bad news for writers like me (or not).

As we transition into a new year, its worth noting that 73% of global consumers say they are open to businesses using AI if it makes life easier, and 83% of businesses say that AI is a strategic priority for their businesses already. If thats not a recipe for even more progress in 2020 and beyond, then my name isnt CMSWire-Bot-927.

Today, were looking back at the AI and ML articles which resonated with CMSWire's audience in 2019. Strap yourself in, because this list is about to blast you into the future.

ML and, more broadly, AI have become the tech industry's most important trends over the past 18 months. And despite the hype and, to some extent, fear surrounding the technology, many businesses are now embracing AI at an impressive speed.

Despite this progress, many of the pilot schemes are still highly experimental, and some organizations are struggling to understand how they can really embrace the technology.

As the business world grapples with the potential of AI and machine learning, new ethical challenges arise on a regular basis related to its use.

One area where tensions are being played out is in talent management: a struggle between relying on human expertise or in deferring decisions to machines so as to better understand employee needs, skills and career potential.

Marketing technology has evolved rapidly over the past decade, with one of the most exciting developments being the creation of publicly-available, cost-effective cognitive APIs by companies like Microsoft, IBM, Alphabet, Amazon and others. These APIs make it possible for businesses and organizations to tap into AI and ML technology for both customer-facing solutions as well as internal operations.

The workplace chatbots are coming! The workplace chatbots are coming!

OK, well, theyre already here. And in a few years, there will be even more. According to Gartner, by 2021 the daily use ofvirtual assistants in the workplacewill climb to 25%. That will be up from less than 2% this year.Gartneralso identified a workplace chatbot landscape of more than 1,000 vendors, so choosing a workplace chatbot wont be easy. IT leaders need to determine the capabilities they need from such a platform in the short term and select a vendor on that basis, according to Gartner.

High-quality metadata plays an outsized role in improving enterprise search results. But convincing people to consistently apply quality metadata has been an uphill battle for most companies. One solution that has been around for a long time now is to automate metadata's creation, using rules-based content auto-classification products.

Although enterprise interest in bots seems to be at an all-time high,Gartner reports that 68%of customer service leaders believe bots and virtual assistants will become even more important in the next two years. As bots are called upon to perform a greater range of tasks, chatbots will increasingly rely on back-office bots to find information and complete transactions on behalf of customers.

If digital workplaces are being disrupted by the ongoing development of AI driven apps, by 2021 those disruptors could end up in their turn being disrupted. The emergence of a new form of AI, or a second wave of AI, known as augmented AI is so significant Gartner predicts that by 2021 it will be creating up to $2.9 trillion of business value and 6.2 billion hours of worker productivity globally.

AI and ML took center stage at IBM Think this year, the shows major AI announcements served as a reminder that the company has some of the most differentiated and competitive services for implementing AI in enterprise operational processes in the market. But if Big Blue is to win the AI race against AWS, Microsoft and Google Cloud in 2019 and beyond, it must improve its developer strategy and strengthen its communications, especially in areas such as trusted AI and governance

Sentiment analysis is the kind of tool a marketer dreams about. By gauging the publics opinion of an event or product through analysis of data on a scale no human could achieve, it gives your team the ability to figure out what people really think. Backed by a growing body of innovative research, sentiment-analysis tools have the ability to dramatically improve your ROI yet many companies are overlooking it.

Pop quiz: Can you define the differences between AI and automation?

I wont judge you if the answer is no. There's a blurry line between AI and automation, with the terms often used interchangeably, even in tech-forward professions. But there's a very real difference between the two and its one thats becoming evermore critical for organizations to understand.

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CMSWire's Top 10 AI and Machine Learning Articles of 2019 - CMSWire

As we dive deeper into the Fourth Industrial Revolution, there is no disputing how technology serves as a catalyst for growth and innovation for many businesses across a range of functions and industries.

But one technology that is steadily gaining prominence across organizations includes machine learning (ML).

In the simplest terms, ML is the science of getting computers to learn and act like humans do without being programmed. It is a form of artificial intelligence (AI) and entails feeding machine data, enabling the computer program to learn autonomously and enhance its accuracy in analyzing data.

The proliferation of technology means AI is now commonplace in our daily lives, with its presence in a panoply of things, such as driverless vehicles, facial recognition devices, and in the customer service industry.

Currently, asset managers are exploring the potential that AI/ML systems can bring to the finance industry; close to 60 percent of managers predict that ML will have a medium-to-large impact across businesses.

MLs ability to analyze large data sets and continuously self-develop through trial and error translates to increased speed and better performance in data analysis for financial firms.

For instance, according to the Harvard Business Review, ML can spot potentially outperforming equities by identifying new patterns in existing data sets and examine the collected responses of CEOs in quarterly earnings calls of the S&P 500 companies for the past 20 years.

Following this, ML can then formulate a review of good and bad stocks, thus providing organizations with valuable insights to drive important business decisions. This data also paves the way for the system to assess the trustworthiness of forecasts from specific company leaders and compare the performance of competitors in the industry.

Besides that, ML also has the capacity to analyze various forms of data, including sound and images. In the past, such formats of information were challenging for computers to analyze, but todays ML algorithms can process images faster and better than humans.

For example, analysts use GPS locations from mobile devices to pattern foot traffic at retail hubs or refer to the point of sale data to trace revenues during major holiday seasons. Hence, data analysts can leverage on this technological advancement to identify trends and new areas for investment.

It is evident that ML is full of potential, but it still has some big shoes to fil if it were to replace the role of an investor.

Nishant Kumar aptly explained this in Bloomberg, Financial data is very noisy, markets are not stationary and powerful tools require deep understanding and talent thats hard to get. One quantitative analyst, or quant, estimates the failure rate in live tests at about 90 percent. Man AHL, a quant unit of Man Group, needed three years of workto gain enough confidence in a machine-learning strategy to devote client money to it. It later extended its use to four of its main money pools.

In other words, human talent and supervision are still essential to developing the right algorithm and in exercising sound investment judgment. After all, the purpose of a machine is to automate repetitive tasks. In this context, ML may seek out correlations of data without understanding their underlying rationale.

One ML expert said, his team spends days evaluating if patterns by ML are sensible, predictive, consistent, and additive. Even if a pattern falls in line with all four criteria, it may not bear much significance in supporting profitable investment decisions.

The bottom line is ML can streamline data analysis steps, but it cannot replace human judgment. Thus, active equity managers should invest in ML systems to remain competitive in this innovate or die era. Financial firms that successfully recruit professionals with the right data skills and sharp investment judgment stands to be at the forefront of the digital economy.

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Can machine learning take over the role of investors? - TechHQ

Deep Learning

One of the factors often credited for this latest boom in artificial intelligence (AI) investment, research, and related cognitive technologies, is the emergence of deep learning neural networks as an evolution of machine algorithms, as well as the corresponding large volume of big data and computing power that makes deep learning a practical reality. While deep learning has been extremely popular and has shown real ability to solve many machine learning problems, deep learning is just one approach to machine learning (ML), that while having proven much capability across a wide range of problem areas, is still just one of many practical approaches. Increasingly, were starting to see news and research showing the limits of deep learning capabilities, as well as some of the downsides to the deep learning approach. So are peoples enthusiasm of AI tied to their enthusiasm of deep learning, and is deep learning really able to deliver on many of its promises?

The Origins of Deep Learning

AI researchers have struggled to understand how the brain learns from the very beginnings of the development of the field of artificial intelligence. It comes as no surprise that since the brain is primarily a collection of interconnected neurons, AI researchers sought to recreate the way the brain is structured through artificial neurons, and connections of those neurons in artificial neural networks. All the way back in 1940, Walter Pitts and Warren McCulloch built the first thresholded logic unit that was an attempt to mimic the way biological neurons worked. The Pitts and McCulloch model was just a proof of concept, but Frank Rosenblatt picked up on the idea in 1957 with the development of the Perceptron that took the concept to its logical extent. While primitive by todays standards, the Perceptron was still capable of remarkable feats - being able to recognize written numbers and letters, and even distinguish male from female faces. That was over 60 years ago!

Rosenblatt was so enthusiastic in 1959 about the Perceptrons promises that he remarked at the time that the perceptron is the embryo of an electronic computer that [we expect] will be able to walk, talk, see, write, reproduce itself and be conscious of its existence. Sound familiar? However, the enthusiasm didnt last. AI researcher Marvin Minsky noted how sensitive the perceptron was to small changes in the images, and also how easily it could be fooled. Maybe the perceptron wasnt really that smart at all. Minsky and AI researcher peer Seymour Papert basically took apart the whole perceptron idea in their Perceptrons book, and made the claim that perceptrons, and neural networks like it, are fundamentally flawed in their inability to handle certain kinds of problems notably, non-linear functions. That is to say, it was easy to train a neural network like a perceptron to put data into classifications, such as male/female, or types of numbers. For these simple neural networks, you can graph a bunch of data and draw a line and say things on one side of the line are in one category and things on the other side of the line are in a different category, thereby classifying them. But theres a whole bunch of problems where you cant draw lines like this, such as speech recognition or many forms of decision-making. These are nonlinear functions, which Minsky and Papert proved perceptrons incapable of solving.

During this period, while neural network approaches to ML settled to become an afterthought in AI, other approaches to ML were in the limelight including knowledge graphs, decision trees, genetic algorithms, similarity models, and other methods. In fact, during this period, IBMs DeepBlue purpose-built AI computer defeated Gary Kasparov in a chess match, the first computer to do so, using a brute-force alpha-beta search algorithm (so-called Good Old-Fashioned AI [GOFAI]) rather than new-fangled deep learning approaches. Yet, even this approach to learning didnt go far, as some said that this system wasnt even intelligent at all.

Yet, the neural network story doesnt end here. In 1986, AI researcher Geoff Hinton, along with David Rumelhart and Ronald Williams, published a research paper entitled Learning representations by back-propagating errors. In this paper, Hinton and crew detailed how you can use many hidden layers of neurons to get around the problems faced by perceptrons. With sufficient data and computing power, these layers can be calculated to identify specific features in the data sets they can classify on, and as a group, could learn nonlinear functions, something known as the universal approximation theorem. The approach works by backpropagating errors from higher layers of the network to lower ones (backprop), expediting training. Now, if you have enough layers, enough data to train those layers, and sufficient computing power to calculate all the interconnections, you can train a neural network to identify and classify almost anything. Researcher Yann Lecun developed LeNet-5 at AT&T Bell Labs in 1998, recognizing handwritten images on checks using an iteration of this approach known as Convolutional Neural Networks (CNNs), and researchers Yoshua Bengio and Jrgen Schmidhube further advanced the field.

Yet, just as things go in AI, research halted when these early neural networks couldnt scale. Surprisingly very little development happened until 2006, when Hinton re-emerged onto the scene with the ideas of unsupervised pre-training and deep belief nets. The idea here is to have a simple two-layer network whose parameters are trained in an unsupervised way, and then stack new layers on top of it, just training that layers parameters. Repeat for dozens, hundreds, even thousands of layers. Eventually you get a deep network with many layers that can learn and understand something complex. This is what deep learning is all about: using lots of layers of trained neural nets to learn just about anything, at least within certain constraints.

In 2010, Stanford researcher Fei-Fei Li published the release of ImageNet, a large database of millions of labeled images. The images were labeled with a hierarchy of classifications, such as animal or vehicle, down to very granular levels, such as husky or trimaran. This ImageNet database was paired with an annual competition called the Large Scale Visual Recognition Challenge (LSVRC) to see which computer vision system had the lowest number of classification and recognition errors. In 2012, Geoff Hinton, Alex Krizhevsky, and Ilya Sutskever, submitted their AlexNet entry that had almost half the number of errors as all previous winning entries. What made their approach win was that they moved from using ordinary computers with CPUs, to specialized graphical processing units (GPUs) that could train much larger models in reasonable amounts of time. They also introduced now-standard deep learning methods such as dropout to reduce a problem called overfitting (when the network is trained too tightly on the example data and cant generalize to broader data), and something called the rectified linear activation unit (ReLU) to speed training. After the success of their competition, it seems everyone took notice, and Deep Learning was off to the races.

Deep Learnings Shortcomings

The fuel that keeps the Deep Learning fires roaring is data and compute power. Specifically, large volumes of well-labeled data sets are needed to train Deep Learning networks. The more layers, the better the learning power, but to have layers you need to have data that is already well labeled to train those layers. Since deep neural networks are primarily a bunch of calculations that have to all be done at the same time, you need a lot of raw computing power, and specifically numerical computing power. Imagine youre tuning a million knobs at the same time to find the optimal combination that will make the system learn based on millions of pieces of data that are being fed into the system. This is why neural networks in the 1950s were not possible, but today they are. Today we finally have lots of data and lots of computing power to handle that data.

Deep learning is being applied successfully in a wide range of situations, such as natural language processing, computer vision, machine translation, bioinformatics, gaming, and many other applications where classification, pattern matching, and the use of this automatically tuned deep neural network approach works well. However, these same advantages have a number of disadvantages.

The most notable of these disadvantages is that since deep learning consists of many layers, each with many interconnected nodes, each configured with different weights and other parameters theres no way to inspect a deep learning network and understand how any particular decision, clustering, or classification is actually done. Its a black box, which means deep learning networks are inherently unexplainable. As many have written on the topic of Explainable AI (XAI), systems that are used to make decisions of significance need to have explainability to satisfy issues of trust, compliance, verifiability, and understandability. While DARPA and others are working on ways to possibly explain deep learning neural networks, the lack of explainability is a significant drawback for many.

The second disadvantage is that deep learning networks are really great at classification and clustering of information, but not really good at other decision-making or learning scenarios. Not every learning situation is one of classifying something in a category or grouping information together into a cluster. Sometimes you have to deduce what to do based on what youve learned before. Deduction and reasoning is not a fort of deep learning networks.

As mentioned earlier, deep learning is also very data and resource hungry. One measure of a neural networks complexity is the number of parameters that need to be learned and tuned. For deep learning neural networks, there can be hundreds of millions of parameters. Training models requires a significant amount of data to adjust these parameters. For example, a speech recognition neural net often requires terabytes of clean, labeled data to train on. The lack of a sufficient, clean, labeled data set would hinder the development of a deep neural net for that problem domain. And even if you have the data, you need to crunch on it to generate the model, which takes a significant amount of time and processing power.

Another challenge of deep learning is that the models produced are very specific to a problem domain. If its trained on a certain dataset of cats, then it will only recognize those cats and cant be used to generalize on animals or be used to identify non-cats. While this is not a problem of only deep learning approaches to machine learning, it can be particularly troublesome when factoring in the overfitting problem mentioned above. Deep learning neural nets can be so tightly constrained (fitted) to the training data that, for example, even small perturbations in the images can lead to wildly inaccurate classifications of images. There are well known examples of turtles being mis-recognized as guns or polar bears being mis-recognized as other animals due to just small changes in the image data. Clearly if youre using this network in mission critical situations, those mistakes would be significant.

Machine Learning is not (just) Deep Learning

Enterprises looking at using cognitive technologies in their business need to look at the whole picture. Machine learning is not just one approach, but rather a collection of different approaches of various different types that are applicable in different scenarios. Some machine learning algorithms are very simple, using small amounts of data and an understandable logic or deduction path thats very suitable for particular situations, while others are very complex and use lots of data and processing power to handle more complicated situations. The key thing to realize is that deep learning isnt all of machine learning, let alone AI. Even Geoff Hinton, the Einstein of deep learning is starting to rethink core elements of deep learning and its limitations.

The key for organizations is to understand which machine learning methods are most viable for which problem areas, and how to plan, develop, deploy, and manage that machine learning approach in practice. Since AI use in the enterprise is still continuing to gain adoption, especially these more advanced cognitive approaches, the best practices on how to employ cognitive technologies successfully are still maturing.

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Are We Overly Infatuated With Deep Learning? - Forbes