Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing operations forward.

During the past decade, enterprises built up massive stores of information on everything from business processes to inventory stats. This was the big data revolution.

But simply storing and managing big data isn’t enough for organizations to get the most value from all that information. As companies master big data management, forward-thinking ones are applying increasingly intelligent or advanced forms of big data analytics to extract even more value from that information. In particular, they are applying machine learning which can spot patterns and provide cognitive capabilities across large volumes of data, giving these organizations the ability to apply the next level of analytics needed to extract value from their data.

How are AI and big data related?

Using machine learning algorithms for big data is a logical step for companies looking to maximize the potential of big data. Machine learning systems use data-driven algorithms and statistical models to analyze and find patterns in data. This is different from traditional rules-based approaches that follow explicit instructions. Big data provides the raw material by which machine learning systems can derive insights. Many organizations are now realizing the benefit of combining big data and machine learning. However, in order for companies to fully utilize the power of both big data and machine learning, it’s important to have an understanding of what each can do on its own.

Big data embodies the idea of extracting and analyzing information from large quantities of data. However, the quantity of data, or its volume, is just one of the considerations in dealing with big data. There are many other important “Vs” of big data that enterprises need to deal with including velocity, variety, veracity, validity, visualization and value.

Machine learning, the cornerstone of modern AI applications, provides considerable value to big data applications by deriving higher level insights from big data. Machine learning systems are able to learn and adapt over time without following explicit instructions or programmed code. These machine learning systems use statistical models to analyze and draw inferences from patterns in data. In the past, companies built complex, rules-based systems for a vast range of reporting needs, but found these solutions were brittle and unable to handle continual changes. Now, with the power of machine learning, and deep learning, companies are able to have systems learn on their big data, improving decision-making, business intelligence and predictive analysis over time.

How does AI benefit big data?

AI, coupled with big data, is impacting businesses across a variety of sectors and industries. Some of the benefits include the following:

• 360-degree view of the customer. Our digital footprints are growing at an astounding rate and companies are using this to their advantage to provide greater insights into each individual. Companies used to move data into and out of data warehouses and create static reports that take a long time to generate and even longer to modify. Now, smart organizations are utilizing distributed, automated and intelligent analytics tools that sit on top of data lakes designed to collect and synthesize data from disparate sources at once. This is transforming the way companies understand their customers.
• Improved forecasting and price optimization. Traditionally, companies base their estimate of current year’s sales on data from the prior year. However, due to a variety of factors such as changing trends, global pandemics or other hard-to-predict factors, forecasting and price optimization can be quite difficult with traditional approaches. Big data is giving organizations the power to spot patterns and trends early and know how those trends will impact future performance. It’s helping companies make better decisions by giving organizations more information about what could potentially happen in the future with greater likelihood. Companies using big data and AI-based approaches, especially in retail, are able to improve seasonal forecasting, reducing errors by as much as 50 percent.
• Improved customer acquisition and retention. With big data and AI, organizations have a better handle on what their customers are interested in, how products and services are being used and reasons why customers stop purchasing or using their offerings. Through big data applications, companies can more accurately identify what customers are really looking for and observe their behavioral patterns. They can then apply those patterns to improve products, generate better conversions, improve brand loyalty, spot trends earlier or find additional ways to improve overall customer satisfaction.
• Cybersecurity and fraud prevention. Tackling fraud is a never-ending battle for businesses of all shapes and sizes. Organizations using big data-powered analytics to identify patterns of fraud are able to detect anomalies in system behavior and thwart bad actors. Big data systems have the power to comb through very large quantities of data from transactional or log data, databases and files to identify, prevent, detect and mitigate potential fraudulent behavior. These systems can also combine a variety of data types including both internal and external data to alert companies to cybersecurity threats that haven’t yet shown up in their own systems. Without big data processing and analysis capabilities, this would be impossible.
• Identifying and mitigating potential risks. Anticipating, planning and responding to constant changes and risks is critical to the longevity of any business. Big data is proving its value in the risk management arena, providing early visibility to potential risks, helping to quantify the exposure to risks and potential losses, and expedite changes. Big data-powered models are also helping organizations identify and address customer and market risks as well as challenges emerging from unpredicted events such as natural disasters. Companies can digest information from disparate data sources and synthesize the information to provide greater situational awareness and understanding of how to allocate people or resources to deal with emerging threats.

How does AI improve insight into data?

Big data and machine learning aren’t really competing concepts and, when combined, they provide the opportunity for some incredible results. Emerging big data approaches are giving organizations powerful ways to store, manage, process and make sense of their data. Machine learning systems learn from that data. In fact, successfully dealing with the various “Vs” of big data will help make machine learning models more accurate and powerful. Machine learning models learn from data and translate these insights to help improve business operations. Likewise, big data management approaches improve machine learning systems by giving these models the large quantity of high quality, relevant data needed to build those models.

The amount of data generated will continue to grow at an astounding rate. By 2025, IDC predicts that worldwide data will grow 61% to 175 zettabytes and that 75% of the world’s population will interact with data daily. As enterprises continue to store huge volumes of data, the only way they will even possibly be able to make sense of it is with the help of machine learning. The machine learning process will come to rely heavily on big data and companies that do not leverage machine learning will be left behind.

Examples of AI and big data

Many organizations have discovered the power of machine learning-enhanced big data analytics and are using the power of big data and AI in a variety of ways.

• Netflix uses machine learning algorithms to help better understand each individual user, providing more personalized recommendations. This keeps the user on their platform for longer and creates a more positive overall customer experience.
• Google uses machine learning to provide users with a highly valuable and personalized experience. They are using machine learning in a variety of products including providing predictive text in emails and optimized directions for users looking to get to a designated location.
• Starbucks is using the power of big data, AI and natural language processing to provide personalized emails using data from customers’ past purchases. Rather than crafting only a few dozen emails on a monthly basis with offers for the broad Starbucks audience, Starbucks is using its “digital flywheel” with AI-enabled capabilities to generate over 400,000 personalized weekly emails featuring different promotions and offers.

Companies are going to continue to combine the power of machine learning, big data, visualization tools and analytics to help their businesses with decision-making through the analysis of raw data. Without big data, none of these more personalized experiences would be possible. In the years ahead it will be no surprise that companies that do not combine big data and AI will have a hard time meeting their digital transformation needs and be left behind.

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Demand for machine learning is skyrocketing. This growth is driven not only by “middle adopters” recognizing the vast potential of machine learning after watching early adopters benefit from its use, but by steady improvements in machine-learning technology itself. It may be too early to say with certainty that machine learning develops according to a predictable framework like Moore’s Law, the famous precept about computing power that has borne out for nearly 50 years and only recently began to show signs of strain. But the industry is clearly on a fast track.

As machine-learning algorithms grow smarter and more organizations come around to the idea of integrating this powerful technology into their processes, it’s high time your enterprise thought about putting machine learning to work, too.

First, consider the benefits and costs. It’s quite likely that your business could leverage at least one of these five reasons to employ machine learning, whether it’s taming apparently infinite amounts of unstructured data or finally personalizing your marketing campaigns.

1. Taming vast unstructured data with limited resources

One of the best-known use cases for machine learning is processing data sets too large for traditional data crunching methods to handle. This is increasingly important as data becomes easier to generate, collect and access, especially for smaller B2C enterprises that often deal with more transaction and customer data than they can manage with limited resources.

How you use machine learning to process and “tame” your data will depend on what you hope to get from that data. Do you want help making more informed product development decisions? To better market to your customers? To acquire new customers? To analyze internal processes that could be improved? Machine learning can help with all these problems and more.

2. Automating routine tasks 

The original promise of machine learning was efficiency. Even as its uses have expanded beyond mere automation, this remains a core function and one of the most commercially viable use cases. Using machine learning to automate routine tasks, save time and manage resources more effectively has a very attractive paid of side effects for enterprises that do it effectively: reducing expenses and boosting net income.

The list of tasks that machine learning can automate is long. As with data processing, how you use machine learning for process automation will depend on which functions exert the greatest drag on your time and resources.

Need ideas? Machine learning has shown encouraging real-world outcomes when used to automate data classification, report generation, IT threat monitoring, loss and fraud prevention and internal auditing. But the possibilities are truly endless.

3. Improving marketing personalization and efficiency

Machine learning is a powerful force multiplier in marketing campaigns, enabling virtually endless messaging and buyer-profile permutations, unlocking the gate to fully personalized marketing without demanding an army of copywriters or publicity agents.

What’s especially encouraging for smaller businesses without much marketing expertise is that machine learning’s potential is baked into the top everyday digital-advertising platforms, namely Facebook and Google. You don’t have to train your own algorithms to use this technology in your next microtargeting campaign.

4. Addressing business trends 

Machine learning has also proven its worth in detecting trends in large data sets. These trends are often too subtle for humans to tease out, or perhaps the data sets are simply too large for “dumb” programs to process effectively.

Whatever the reason for machine learning’s success in this space, the potential benefits are clear as day. For example, many small and midsize enterprises use machine learning technology to predict and reduce customer churn, looking for signs that customers are considering competitors and trigger retention processes with higher probabilities of success.

Elsewhere, companies of all sizes are getting more comfortable integrating machine learning into their hiring processes. By reinforcing existing biases in human-led hiring and promotion, earlier-generation algorithms did more harm than good, but newer models are able to counteract implicit bias and increase the chances of equitable outcomes.

5. Accelerating research cycles

A machine-learning algorithm unleashed in an R&D department is like an army of super-smart lab assistants. As more and more enterprises discover just what machine learning is capable of in and out of the lab, they’re feeling more confident about using it to eliminate some of the frustrating trial-and-error that lengthens research cycles and increases development costs. Machine learning won’t replace R&D experts anytime soon, but it does appear to empower them to use their time more effectively. More and better innovations could result.

If the experience of competitor businesses that have already deployed machine learning to great effect is any guide for your own experience, the answer to this question is a resounding yes.

The more interesting question is how you choose to make machine learning work for your businesses. This prompts another question, around what operational and structural changes your machine learning processes will bring. These changes, up to and including reducing headcounts in redundant roles or winding up entire lines of business, could be painful in the short run even as they strengthen your enterprise for the long haul.

Like all great innovations that increase operational efficiency and eliminate low-value work, machine learning does not benefit everyone equally. It’s up to the humans in charge of these algorithms to make the transition as orderly and painless as possible. It seems there are some things machine learning can’t yet do … yet.

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“Depending on who you ask, AI is either man’s greatest invention since the discovery of fire”, as Google’s CEO said at Google’s I/O 2017 keynote, or it is a technology that might one day make man superfluous. What’s inarguable is major companies have embraced AI as if it was one of the most important discoveries ever invented. In the US, Amazon, Apple, Microsoft, Facebook, IBM, SAS, and Adobe have all infused AI and machine learning throughout their operations, while in China the big four – Baidu, Alibaba, Tencent, Xiaomi – are coordinating with the government and all working on unique and almost siloed AI initiatives.

In her article Understanding Three Types of Artificial Intelligence, Anjali UJ explains “The term AI was coined by John McCarthy, an American computer scientist in 1956.” Anjali speaks of the following three types of AI, including:

1. Narrow Artificial Intelligence: AI that has been trained for a narrow task.
2. Artificial General Intelligence: AI containing generalized cognitive abilities, which understand and reason the environment the way humans do.
3. Artificial Super Intelligence: AI that surpasses human intelligence and allows machines to mimic human thought.

AI is not a new technology, in reality, it’s decades old. In his MIT Technology Review article Is AI Riding a One-Trick Pony?, James Somers states “Just about every AI advance you’ve heard of depends on a breakthrough that’s three decades old.” Recent advances in chip technology, as well as improvements in hardware, software, and electronics have turned AI’s enormous potential into reality.

Neural Nets

AI is founded on Artificial Neural Networks (ANN) or just “Neural Nets”, which are non-linear statistical data modelling tools used when the true nature of a relationship between input and output is unknown. In his article Machine Learning Applications for Data Center Optimization, Jim Gao describes neural nets as “a class of machine learning algorithms that mimic cognitive behavior via interactions between artificial neurons.” Neural nets search for patterns and interactions between features to automatically generate a best­ fit model.

They do not require the user to predefine a model’s feature interactions. Speech recognition, image processing, chatbots, recommendation systems, and autonomous software agents are common examples of machine learning. There are three types of training in neural networks; supervised, which is the most common, as well as unsupervised training and reinforcement learning. AI can be broken down into three areas:

Machine Learning

A branch of computer science, machine learning explores the composition and application of algorithms that learn from data. These algorithms build models based on inputs and use those results to predict or determine actions and results, rather than following strict instructions.

Supervised learning’s goal is to learn a general rule that maps inputs to outputs and the computer is provided with example inputs as well as the desired outputs. With unsupervised learning, however, labeled data isn’t provided to the learning algorithm and it must find the input’s structure on its own. In reinforcement learning, the computer utilizes trial and error to solve a problem. Like Pavlov’s dog, the computer is rewarded for good actions it performs and the goal of the program is to maximize reward.

Deep learning

A subset of machine learning, deep learning utilizes multi-layered neural nets to perform classification tasks directly from image, text, and/or sound data. In some cases, deep learning models are already exceeding human-level performance. Google Meet’s ability to transcribe a human voice during a live conference call is an example of deep learning’s impressive capabilities.

ML and deep learning are useful for personalization marketing, customer recommendation, spam filtering, fraud detection, network security, optical character recognition (OCR), computer vision, voice recognition, predictive asset maintenance, sentiments analysis, language translations, and online search, among others.

7 Patterns of AI

In her Forbes article The Seven Patterns of AI, Kathleen Walch lays out a theory that, regardless of the application of AI, there are seven commonalities to all AI applications. These are “hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems.” Walch adds that, while AI might require its own programming and pattern recognition, each type can be combined with others, but they all follow their own pretty standard set of rules.

The ‘Hyperpersonalization Pattern’ can be boiled down to the slogan, ‘Treat each customer as an individual’. ‘Autonomous systems’ will reduce the need for manual labor. Predictive analytics portends “some future value for data, predicting behavior, predicting failure, assisted problem resolution, identifying and selecting best fit, identifying matches in data, optimization activities, giving advice, and intelligent navigation,” says Walch. The ‘Conversational Pattern’ includes chatbots, which allow humans to communicate with machines via voice, text, or image.

The ‘Patterns and Anomalies’ type utilizes machine learning to discern patterns in data and it attempts to discover higher-order connections between data points, explains Walch. The recognition pattern helps identify and determine objects within image, video, audio, text, or other highly unstructured data notes Walch. The ‘Goal-Driven Systems Pattern’ utilizes the power of reinforcement learning to help computers beat humans on some of the most complex games imaginable, including Go and Dota 2, a complicated multiplayer online battle arena video game.

^Conclusion

A few years ago, the AI hype had reached such a fever pitch that companies just had to add ‘AI’, ‘ML’, or ‘Deep Learning’ to their pitch decks, and funding flooded through the door. However, businesses are investing in AI powered solutions like AIOps to reduce IT operations cost. Today, investors are a little wiser to the fact that not all that glitters is AI gold, and a lot of companies who pitched themselves as AI experts really didn’t know the difference between a neural net and a k-means algorithm.

Jumping head-first into AI is a recipe for disaster. Only “1 in 3 AI projects are successful and it takes more than 6 months to go from concept to production, with a significant portion of them never making it to production—creating an AI dilemma for organizations,” says Databricks. Not only is AI old, but it is also a difficult technology to implement. Anyone delving into AI needs to have a strong understanding of technology, what it is, where it came from, what limitations might hold it back, so although AI is exceptional technology, the waters are deep. It is far from the panacea that many software companies claim it is. AI has had not one but two AI winters. CEOs looking to make a substantial investment in AI should be well aware of the old saying that ‘a fool and his money are easily parted’, as that fool could be an AI fool, too.

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Earlier this year, Microsoft had invited us to its Bengaluru campus for a two-day briefing on how it’s incorporating artificial intelligence (AI) in many of its business solutions, including Azure, Power BI, Teams, and Office 365. In addition to letting a few of its business partners explain how these AI-enabled services help them, the Redmond-based software giant had demonstrated its Garage-developed apps such as Kaizala, Seeing AI, and Soundscape.

In a style quite similar to Microsoft’s, Google invited us to its Roppongi Hills office in Tokyo for a one-day briefing titled, “Solve… with AI” earlier this week. The briefing was headed by Jeff Dean, a Senior Fellow and AI Lead at Google. While Microsoft’s briefing on AI mostly revolved around solutions that tackle IT business challenges, Google’s briefing addressed solutions aimed towards the “social good”. Product leads from Google AI explained how the company’s technology is being put to use in areas like healthcare, environmental conservation, agriculture, and others. Google invited a few of its business partners to add inputs and examples during the briefing.

Introduction

The briefing began with Dean delivering the keynote address in which he explained the basics of machine learning (ML), which is a subset of AI that involves training a computer to recognise patterns by example, rather than programming it with specific rules. He explained how neural networks can be trained to identify patterns that are either too vast or complex for humans with the use of relatively simple mathematical functions. ML models are developed for this purpose.

Apart from employing them in its own products, Google offers ML tools along with some reference implementation information to researchers and developers to build AI-enabled software. Examples of such tools include the open-source TensorFlow software library, CloudML platform, Cloud Vision API, Cloud Translate API, Cloud Speech API, and Cloud Natural Language API. Google incorporates ML models in its offerings, including Search, Photos, Translate, Gmail, YouTube, Chrome, etc.

Dean used the example of an air quality monitoring tool called Air Cognizer to demonstrate how TensorFlow is used in everyday mobile app development. Air Cognizer is an app developed in India as part of Celestini Project India 2018. It can help detect the air quality level of the surrounding area by scanning a picture taken through the Android device’s camera. Dean went on to say that that was only one such example of developers and researchers using Google’s machine learning tools to create AI-enabled apps and services. After Dean’s introduction, other Google AI team leaders took the stage one by one to talk about other areas in which Google’s ML efforts are making a difference.

Healthcare

Lily Peng, Product Manager for Google Health, came on stage after Dean’s introduction to talk about how Google’s AI ventures help in the field of healthcare. “We believe that technology can have a big impact in medicine, helping democratize access to care, returning attention to patients and helping researchers make scientific discoveries,” she said during her presentation. She supported her statement by citing three specific areas in which Google’s ML models are seeing success: lung cancer screening, breast cancer metastases detection, and diabetic eye disease detection.

Google’s ML model can, according to the company, analyse CT scans and predict lung malignancies in cancer screening tests. In the tests conducted by Google, the company’s model detected 5 percent more cancer cases, thereby reducing false positives by over 11 percent compared to radiologists. According to Google, early diagnosis can go a long way in treating the deadly disease but over 80 percent of lung cancers are not caught early.

In breast cancer metastases detection, Google says its ML model can find 95 percent of cancer lesions in pathology images. Google claims that pathologists can generally only detect 73 percent of cancer lesions. Its model can scan medical slides better, which are each up to 10 GigaPixels in size. Google says it’s also more successful in detecting false positives than doctors. Google says that it has found that the combination of pathologists and AI was more accurate than either alone.

Google says that, with the help of its sister company Verily, it’s becoming increasingly more successful in treating diabetic retinopathy. The company is currently piloting the use of its ML model for detection of cases of diabetic retinopathy in India and Thailand. Google believes that there’s a shortage of doctors and special equipment in many places, which is one of the reasons the disease isn’t caught early, leading to lifelong blindness amongst patients.

Environmental conservation

Julie Cattiau, a Product Manager at Google AI, explained how wildlife on the planet has decreased by 58 percent in the past half a century. According to her, Google’s AI technology is currently helping conservationists track the sound of humpback whales, an at-risk marine species, in order to prevent losing them altogether to extinction. In one bioacoustics project, Google has apparently partnered with NOAA (National Oceanic and Atmospheric Administration), which has collected over 19 years worth of underwater audio data so far.

Google says that it was able to train its neural network (or “whale classifier”) to identify the call of a humpback whale within that 19-year-long audio data set. During her presentation, Cattiau said that this was a big challenge for the researchers partly because the sound of a humpback whale can easily be mistaken for that of another type of whale or ships passing by. Google believes that its AI technology was successful and helpful in the project as listening for the call of a whale in a data set that vast is a task that would take a human being an inordinate amount of time to complete.

Topher White, the CEO of Rainforest Connection, was one of the many partners invited by Google to participate in the briefing. With the use of a proprietary technology, Rainforest Connection prevents illegal deforestation by listening for sounds of chainsaws and logging trucks in rainforests across ten countries and alerting local authorities. Its technology involves the use of refurbished solar-charged Android smartphones that use Google TensorFlow to analyse the auditory data in real-time from within a rainforest. According to White, deforestation is a bigger cause of climate change than pollution caused by vehicles.

Febriadi Pratama, the Co-Founder of Gringgo Indonesia Foundation, was another one of the many partners invited by Google for the briefing. The foundation, which is a recipient of the Google AI Impact Challenge, is currently using Google’s ML models to identify types of waste material using image recognition in the Indonesian city of Denpasar. Pratama said during his speech that the project was effectively helping the foundation rake up plastic in a city where there’s no formal system for waste management.

Agriculture

Raghu Dharmaraju, Vice President of Products & Programs at the Wadhwani Institute for Artificial Intelligence, was also one of the partners invited by Google to participate in the briefing. The institute uses a proprietary Android app along with pheromone traps to scan samples of crops for signs of pests, which, in a large farm in India, can potentially wreck a farmer’s harvest. The app uses ML models developed by Google. In his presentation, Dharmaraju said that the solution developed by the institute was notably effective in detecting pink bollworms in cotton crops in India.

Flood forecasting

Sella Nevo, a Software Engineering Manager at Google AI, took the stage to talk about the company’s flood forecasting initiative. According to him, dated, low-resolution elevation maps make it hard to predict floods in any given area. SRTM, the provider of elevation maps, hands out data that’s nearly two decades old, he said during his presentation. In a pilot project started last year in Patna, Google was able to produce high-definition elevation maps using its ML models with the help of data taken from satellites and other sources in order to forecast floods. It was then able to alert its users about a flood incident in Gandhi Ghat. The flood alert was sent out as a notification on smartphones.

“The number one issue is access to data, and we have tried to tackle that. With different types of data, we find different solutions. So, for the elevation maps, the data just doesn’t exist. So we worked on different algorithms to produce and create that data for stream gauge measurements. For various satellite data, we purchased and aggregated most of it,” Nevo told us in an interview. According to him, Google is trying to produce elevation maps that can be updated every year, unlike the ones given out by SRTM.

Accessibility

Sagar Savla, a Product Manager at Google AI, took the stage to talk about Google’s Live Transcribe app. Available in 70 languages currently, the app helps the deaf and hard-of-hearing communicate with others by transcribing speech in the real world to on-screen text. The app is developed using Google’s ML models to ensure precision in its transcription. For example, the app can tell whether the user means to say “New Jersey” or “a new jersey” depending on the context of the sentence. Talking about the app and its development, Savla said that he had used it with his grandmother, who, despite being hard of hearing, was able to join in on the conversation using the Live Transcribe app in Gujarati.

Julie Cattiau returned to the stage to talk about Project Euphonia, a Google initiative dedicated to building speech models that are trained to understand people with impaired speech. The initiative could in the future combine speech with computer vision, she said during her presentation. For example, people who suffer from speech impairments caused by neurological conditions could use gestures such as blinking to communicate with others. Cattiau said that the company’s ML models are currently being trained to recognise more gestures.

Cultural Preservation

Tarin Clanuwat, a Project Researcher at the ROIS-DS Center for Open Data in the Humanities, went on stage about an ancient cursive Japanese script called Kuzushiji. Although there are millions of books and over a billion historical documents recorded in Kuzushiji, less than 0.01 percent of the population can read it fluently today, she said during her presentation. She fears that this cultural heritage is currently at risk of becoming inaccessible in the future owing to disuse in modern texts.

Google says that Turin and her fellow researchers trained an ML model to recognise Kuzushiji characters and transcribe them into modern Japanese. According to Google, the model takes approximately two seconds to transcribe an entire page and roughly an hour to transcribe an entire book. According to test data, the model is currently capable of detecting about 2,300 character types with an average accuracy of 85 percent. Turin and her team are working towards improving the model in order to preserve the cultural heritage captured in Kuzushiji texts.

Summary

Google seems convinced it’s headed in the right direction when it comes to applying machine learning the right way for social causes. In the future, we can expect Google to take on more such projects, where neural networks are trained to understand data sets that hold keys and clues to hitherto insoluble problems in areas never tried before. At the same time, more and more developers and researchers should be able to incorporate Google’s open-source TensorFlow library in their projects as long as Google continues to provide support and reference material for it.

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