Back in 1955, when the term “artificial intelligence” was first coined by John MacCarthy, the promise of thinking machines as intelligent as human beings was only a generation away. It wasn’t long though before scientists and engineers realized that true machine intelligence was in fact much more complex than originally anticipated and that it would take many more years to materialize.

Decades later with many new innovations in the field, the AI community is once again enthusiastic about all the recent advances and the potential of artificial intelligence. Today, while there may be a debate as to what is real AI or what differentiates one technique or technology from another, it’s clear that there is a growing fervor around this field and its potential for impact in our society.

In this video, Entefy’s Vice President of AI/Machine Learning Engineering, Arnulf Graf, PhD, provides a brief introduction to the emerging area of multimodal AI and the major benefits that can be derived from it.

Unimodal systems (such as those which can separately analyze bodies of text, audio files, or images) can provide great value for select tasks and use cases, however they can lack the breadth necessary to truly generalize and seamlessly scale across domains and topics. This ability to generalize and contextualize learning outside of a narrow domain is something people do exceedingly well, but remains largely elusive to machines. Machines capable of human level cognition may still be a few years out, but it’s clear that mastering multimodal machine learning is a critical step in that journey.

At Entefy, we’re focused on applying advanced multimodal AI to everyday processes and workflows, saving time, increasing productivity, and improving security for people and organizations everywhere.

To learn more about Entefy’s multimodal AI, read our previous post. For other key AI terms and definitions, see our Helpful AI terms for today’s business professional.

Artificial intelligence is making its mark transforming industries, remaking jobs and job markets, and redefining the dynamics of cybersecurity. One thing that doesn’t come up often when we talk about AI is the environment. But it should. Because AI technologies like machine learning have great promise to make manufacturing processes and supply chains more efficient.

Data shows that 70% to 80% of the carbon footprint of a personal computing device like a laptop or smartphone is generated during its manufacturing. To put that in perspective, 17 of the world’s largest consumer electronics companies produce greenhouse gas emissions of around 103 million metric tons of CO2E per year. That’s on par with the total greenhouse gas emissions of a country like Chile.

AI to the rescue. McKinsey projects that the use of machine learning in semiconductor manufacturing can lead to a 30% improvement in efficiency. Not all of that AI-driven efficiency leads directly to reduced energy consumption. But even small improvements can have a big impact. For those 17 electronics companies, every 10% reduction in energy use during manufacturing would save the equivalent of 1.1 billion gallons of gasoline, or 11.2 billion pounds of coal burned, or the equivalent of 1.5 million U.S. homes’ annual electricity use. AI that’s good for the Earth, and good for the bottom line.

Entefy’s enFacts are illuminating nuggets of information about the intersection of communications, artificial intelligence, security and cyber privacy, and the Internet of Things. Have an idea for an enFact? We would love to hear from you.

While technology consistently improves and gives us new and better ways to conduct business and manage our lives, truly groundbreaking inventions are often few and far between.

One such invention is blockchain, the distributed ledger technology on which Bitcoin and other cryptocurrencies are built. The technology is steadily influencing global industries, and it’s changing everything from how energy grids are managed to how medical records are stored. But blockchain isn’t just reshaping how we build, ship, and secure products. It’s also transforming how industry supply chains are structured. And it’s happening fast.

The end of the middleman?

One of the most celebrated features of blockchain is its decentralized structure. When transactions are recorded, no single entity owns the record. All relevant parties can access the information and see exactly when and how a payment or transfer of value was processed. Data isn’t stored in one single location, either. It’s distributed across a series of computers, or nodes, which makes it less vulnerable to corruption. The distributed, decentralized structure reduces the need for an intermediary middleman to transfer information and goods between buyers and sellers.

Here’s an example of how that works. Let’s assume that an entrepreneur decides to open an online business selling handmade, artisanal goods from her hometown. She wants consumers across the country to be able to buy her high-quality products, so she seeks out a shipping company to manage the deliveries. By using a blockchain-based smart contract, she doesn’t need an intermediary to set up the relationship or act as a go-between for her and the shipping vendor. The details of their agreements are recorded on a blockchain, and both parties have encryption keys that allow them and their authorized users to review contracts at any time. Contracts are  virtually tamper-proof and the original agreements, once recorded, are immutable. Everyone involved saves time and money since they don’t need to coordinate – or pay – an intermediary service to facilitate the transactions.

Although middlemen often provide value, there’s no getting around the fact that the more people involved in a process, the longer it takes to complete, the more expensive it is, and the more opportunities there are for mistakes. Every additional layer in a business environment creates costly, and often unpredictable, variability. The hope is that blockchain can significantly reduce transactions costs, frictions, and inefficiencies across the board.

Here are 4 examples of what a world without middlemen might look like:

Pharmaceuticals

Supply chain costs represent a massive expense, largely due to middlemen companies that nurture relationships and serve as mediators between different points in the production pipeline. Companies in a number of industries are beginning to shift their dependence from middlemen to blockchain.

For example, pharma companies have begun exploring the use of blockchain to reduce the number of counterfeit drugs that make it to the market, which constitute 1% of sales in the developed world and up to 70% in poorer countries. In Europe alone, pharmaceutical corporations lose more than $11.5 billion a year due to counterfeits, and at least 40,000 jobs are jeopardized by the crisis. By using blockchain to document and verify drug production and sales at every point in the supply chain, pharma companies can better control the products that are sold to their clients and consumers while also reducing the number of players involved in their supply chains.

Real estate

Commercial real estate transactions are often lengthy and complex endeavors. But blockchain could make the process less burdensome. If all property details, including key information about ownership history and titles, were stored on a blockchain, buyers and sellers could easily access that information without the need for a real estate agent’s assistance.

Instead of relying on agents and title management companies to build trust between buyers and sellers, a blockchain allows them to verify ownership and vet deals without involving one or more third parties. This could mean faster transaction times and decreased transaction costs, as buyers and sellers negotiate directly and avoid paying intermediaries for their involvement.

Energy

To help ease strains on the energy supply, utility companies are experimenting with blockchain by developing platforms that allow individual consumers to buy and sell energy through smart contracts.

One application would be to allow homeowners with solar arrays to sell the power captured via their panels to neighbors who need it. Blockchain platforms allow these exchanges to happen in real-time, with immediate transfers of energy and money. Importantly, such options enable communities to become more resilient against power outages, storms, and other adverse events by developing blockchain-enabled microgrids.  

Banking

Security and efficiency are particular concerns in the financial world, given that banks and other financial institutions are the ones handling corporate and consumer finances. It is in everyone’s interests for banks to adopt safe, cost-effective processes, and blockchain delivers on that. Back-office functions, including accounting, transaction clearing and settlement, and regulatory compliance, are critical to healthy banks. But these departments and processes can also be subject to headcount bloat and runaway costs, particularly as banks attempt to get their arms around growing fraud and cybersecurity risks. By instituting blockchain systems, banks could save $20 billion on back office costs while also securing their clients’ data and financial assets.

Big picture, we’re beginning to see blockchain create new services that can generate efficiency and at times bypass intermediaries. Innovative companies that adopt blockchain have the opportunity to capture more of the value in their supply chain. 

How is the $735 billion U.S. entertainment and media industry responding to constantly changing consumer tastes and revenue declines in several traditional market segments? By getting personal. Very personal. And to do it, they’re leveraging AI and machine learning to develop entirely new forms of personalized user experiences.

Today’s consumers expect increasingly individualized entertainment, a trend that has led to revenue declines in traditional entertainment segments like movie theaters and on-demand rentals. In response, industry leaders are exploring cutting-edge solutions built on AI and machine learning technologies. These early efforts point to a highly personalized future that moves far beyond today’s recommendation engines to individualized content experiences and even custom-generated celebrities.

Here is a look at several high-impact AI and machine learning projects in different segments of the media industry.

Behind-the-scenes machine learning workflow management

Netflix has been a pioneer in the use of machine learning technology to enable personalized user experiences. So much so that their success has raised user expectations to the point where personalized experiences are no longer surprising but simply expected.

Netflix offered a behind-the-scenes look at how it manages its machine learning technology, a system it calls Meson. The system acts as a traffic cop for the company’s multiple ML pipelines that “build, train, and validate personalization algorithms that drive video recommendations.”

Meson is a great example of how a media brand can successfully build upon its early experiments with machine learning personalization to enable sophisticated new capabilities.

The future of AI-generated podcasts

A graduate student at the University of California, Santa Cruz developed an AI-generated “infinite” podcast technology that hints at the future of audience-of-1 entertainment. The project is known as Sheldon County, and works as an algorithmically-generated content engine that provides each individual listener of the podcast their own unique narrative and characters.

The project makes use of symbolic AI to generate rules and logic to fuel the story engine. Users enter a number, or “seed,” that the story engine uses to generate a unique set of story elements. It then uses a language generation system to create the story and a voice synthesizer to read it aloud. Sheldon County is a work in progress. But in an era when media executives complain of skyrocketing production costs, the auto-generated podcast serves as a promising model for both hyper-personalized content and machine-produced creativity.

AI-generated celebrities

In 1990’s Star Trek, crew members retreat to the holodeck to enjoy deeply personal, entirely immersive narrative entertainment. Back on Earth, today we call that virtual reality (VR), even if the nascent technology is far from the ultrarealistic format enjoyed by the crew of the starship Enterprise. Putting aside the limitations of today’s VR hardware, who is going to star in tomorrow’s virtual entertainments? Breakthrough research at NVIDIA suggests that the virtual celebrities of tomorrow will be perfectly cast for each viewer. And entirely computer-generated.

NVIDIA artificial intelligence scientists published a paper describing a new training technique for generative adversarial networks (GANs) that allows these systems to generate realistic celebrities using a vast database of actual celebrities. It’s a short leap from that use case to cutting-edge systems that generate the perfect actors or irresistible product pitchmen tailored for the individual viewer. Removing the fiction from science fiction.

Predicting audience engagement using machine learning

A team of MIT researchers points to another path into the personalized future. The scientists developed a machine learning system that scores the emotional content of filmed entertainment. “We developed machine-learning models that rely on deep neural networks to ‘watch’ small slices of video—movies, TV, and short online features—and estimate their positive or negative emotional content by the second,” the team explained.

The near-term use for this technology is to augment the creative powers of screenwriters and to predict the likely audience engagement with a given script. Yet as we’ve seen with these other examples of advanced personalization, it is possible to imagine machine learning being used to generate entertainment content specifically tailored to the personalities of a narrow audience, even of specific individuals. What better way to garner 5-star reviews than to tailor a film to the audience’s specific tastes?

NBCUniversal explores intelligent content

Machine learning that produces personalized advertisements, for example, can be thought of as first-generation entertainment AI. Current-generation AI might include voice-enabled systems that use natural language processing. NBCUniversal is pushing ahead into next-generation entertainment with intelligent content created using AI. ‘We have very many talented people that create content. AI enables this great content to become intelligent and allows us to access this content across multiple platforms (digital and linear) and in any format from TV to Alexa to Snapchat,’ the company explained.

The entertainment giant is leveraging emerging AI technologies like computer vision (CV) to automatically discover the content and context of video, including data on who is in a scene, what is happening, what is being said, and even the underlying sentiment. NBCUniversal uses this metadata in new products like customized clip generators or advanced search tools. Leveraging AI allows the company to better leverage its video collection. And create next-generation customer-facing services and internal capabilities.

With AI and machine learning supporting new forms of entertainment in virtual and real space, the future of entertainment looks to be a highly personalized one.

If ever there was a case of “greater than the sum of its parts,” you’ll find it with Entefy’s multimodal AI, the machine learning paradigm where computer vision, natural language, audio, and other data intelligence work together to generate advanced capabilities and insights.

As people, we experience the world using a number of senses or modalities that are beautifully integrated to create a holistic understanding of the world. From touch to vision to hearing, our ability to learn is based on a complex, yet seamlessly integrated cognitive process. Much of the success in machine learning so far has been based on learning that involves only a single modality. Good examples include text, audio, or images. But for AI to achieve much better performance and understanding it needs to get better at dealing with cross-modal learning, involving diverse data types. What’s important here isn’t simply the mechanics but rather the artful interpretation, fusion, disambiguation, and knowledge transfer needed when dealing with the multiple modalities and the models that evaluate them.

To see just how powerfully effective the multimodal approach can be, check out this overview of Entefy’s Mimi AI. In this video, Entefy’s Co-Founder Brienne Ghafourifar provides a guided tour of the Mimi platform and hints at the valuable use cases Mimi makes possible.

Artificial intelligence (AI) is quickly making its way into the products and processes at companies, governmental agencies, and nonprofits around the globe. Leaders and stakeholders who are evaluating AI and machine learning solutions face real challenges in keeping up to date with AI technologies, capabilities, the lingo, and the seemingly unlimited potential for use cases. But without deeper AI training and education, it can be hard to keep pace with these fast-emerging areas.

Which is why Entefy assembled this list of helpful AI terms specifically geared towards anyone with an interest in artificial intelligence and machine learning. Be sure to bookmark this page for a handy quick-reference resource.

Algorithm. A procedure or formula, often mathematical, that defines a sequence of operations to solve a problem or class of problems.

Artificial intelligence. The umbrella term for computer systems that can interpret, analyze, and learn from data in ways similar to human cognition.

Chatbot. A computer program (often designed as an AI-powered virtual agent) that provides information or takes actions in response to the user’s voice or text commands or both. Current chatbots are often deployed to provide customer service or support functions.

Cognitive computing. A term that describes advanced AI systems that mimic the functioning of the human brain to improve decisionmaking and perform complex tasks.

Computer vision. An artificial intelligence field focused on classifying and contextualizing the content of digital video and images. 

Data curation. The process of collecting and managing data, including verification, annotation, and transformation.Also see training and dataset.

Data mining. The process of targeted discovery of information, patterns, or context within one or more data repositories.

Deep learning. A subfield of machine learning that uses artificial neural networks with two or more hidden layers to train a computer to process data, recognize patterns, and make predictions.

Ensembling. A powerful technique whereby two or more algorithms, models, or neural networks are combined in order to generate more accurate predictions.

Generative adversarial networks (GANs). A class of AI algorithms whereby two neural networks compete against each other to improve capabilities and become stronger.

Machine learning. A subset of artificial intelligence that gives machines the ability to analyze a set of data, draw conclusions about the data, and then make predictions when presented with new data without being explicitly programmed to do so.

Natural Language Processing (NLP). A field of computer science and artificial intelligence focused on processing and analyzing natural human language or text data.

Natural Language Understanding (NLU). A specialty area within Natural Language Processing focused on advanced analysis of text to extract meaning and context.

Neural networks. A specific technique for doing machine learning that is inspired by the neural connections of the human brain. The intelligence comes from the ability to analyze countless data inputs to discover context and meaning.

Ontology. A data model that represents relationships between concepts, events, entities, or other categories. In the AI context, ontologies are often used by AI systems to analyze, share, or reuse knowledge.

Random forest. An ensemble machine learning method that blends the output of multiple decision trees in order to produce improved results.

Reinforcement learning. A machine learning technique where an agent learns  independently the rules of a system via trial-and-error sequences.

Semi-supervised learning. A machine learning technique that fits between supervised learning (in which data used for training is labeled) and unsupervised learning (in which data used for training is unlabeled).

Strong AI. Theterm used to describe artificial general intelligence or a machine’s intelligence functionality that matches human cognitive capabilities across multiple domains. Often characterized by self-improvement mechanisms and generalization rather than specific training to perform in narrow domains. Also see weak AI.

Structured data. Data that has been organized using a predetermined model, often in the form of a table with values and linked relationships. Also see unstructured data.

Supervised learning. A machine learning technique that infers from training performed on labeled data. Also see unsupervised learning.

Taxonomy. A hierarchal structured list of terms to illustrate the relationship between those terms. Also see ontology. 

Time series. A set of data structured in spaced units of time.

Transfer learning. A machine learning technique where the knowledge derived from solving one problem is applied to a different (typically related) problem.

Training. The process of providing a dataset to a machine learning model for the purpose of improving the precision or effectiveness of the model. Also see supervised learning and unsupervised learning.

Tuning. The process of optimizing the hyperparameters of an AI algorithm to improve its precision or effectiveness. Also see algorithm.

Unstructured data. Data that has not been organized with a predetermined order or structure, often making it difficult for computer systems to process and analyze.

Unsupervised learning. A machine learning technique that infers from training performed on unlabeled data. Also see supervised learning.

Weak AI. Theterm used to describe a narrow AI built and trained for a  specific task. Also see strong AI.

Customer experience (CX for short) broadly defines the quality of all the interactions that take place between companies and their customers. It’s a critical factor in building trust, loyalty, and repeat business.

So anyone interested in CX trends should pay close attention to the latest Customer Experience Index report from Forrester Research. The annual survey asks nearly 120,000 U.S. consumers to rank 287 brands across 19 industries, focusing on how their company-specific experiences impact brand loyalty. The alarming surprise was that the aggregate measurement of customer experiences failed to improve, with more brands than ever ranked “mediocre.” Just 37 brands rose in the rankings; the remaining 250 stagnated or declined.

A key insight from the report is that it is harder than ever to achieve truly memorable customer interactions, the types that turn customers into vocal advocates. But there is a silver lining. Successfully creating seamless experiences can be a significant competitive advantage, especially in industries where product or service differentiation is narrow.

CX-focused brands are deploying artificial intelligence technologies strategically at key customer touch points. To illustrate what that looks like in practice, we’ve assembled 5 examples of AI-powered CX from 5 different industries. Showing that 5-star experiences can be just an algorithm away for forward-thinking companies.

In retail, AI-enabled personalization unlocks access to the 1% customer. Data shows thatthe top 1% of a retailer’s customers are worth 18x more than its average customer. The most effective tool for engaging those discriminating, high-value customers is through personalization. But because basic personalization like user-specific page layouts is already table stakes for memorable customer experience, extreme personalization is needed. And this is where advanced machine learning comes into play. Extreme personalization moves beyond, for instance, a one-off personalized newsletter to customer-tailored promotions that are delivered at the right time, to the right device, and with the perfect message. Think of it as the move from customer segments to the audience of one.

For a global bank, AI builds trust and loyalty. The Royal Bank of Scotland (RBS) manages 17 million customers across 7 brands and 8 different channels. Its historic strategy focused on aggressive sales goals intended to upsell customers into new credit cards. Yet from the customer perspective, this amounted to a heap of digital and paper spam. RBS sought to completely revamp its relationship with the customer, turning to AI to transform customer experience. Its approach was to leverage data intelligence into entirely new forms of customer contact. For instance, when a customer repeatedly overdrafts his or her account, the AI flags the appropriate bank personnel to contact the customer with financial advice. ‘It’s about a continuous conversation,’ said one company executive.

For one airline, new data intelligence drives CX innovation. Air Canada serves 45 million customers annually, with the majority booking online or through its mobile app. Seeking to better understand its customer and, ultimately, improve its mobile app experience, the company deployed an AI and machine learning data analytics system that provided insight into customer behavior across digital and offline channels. Company leaders leveraged the data analytics insight into customer-facing performance enhancements and a streamlined website experience.

In entertainment, AI battles ticket bots. The Internet transformed the dynamics of live events by introducing easy-to-access ticket secondary markets like Craigslist and StubHub. More recently, automated bots buy up large blocks of tickets, depleting supply, then instantly offer those tickets for sale at major markups. For fans seeking to buy tickets to a hot concert or playoff game, the experience is often frustrating, budget-busting, or both. Ticketmaster turned to AI to rewrite the rules using a machine learning system called Verified Fan. The system requires ticket buyers to register their interest before tickets go on sale. Behind the scenes, the AI system analyzes every registrant to identify scalper bots. This resulted in just 5% of tickets sold using Verified Fan ending up on secondary markets. And artists and fans much happier with the ticket buying experience.

For one luxury hotel brand, new insight required new AI. To better understand the customer,the hospitality industry has long used techniques like mystery shoppers and customer surveys. Leveraging the content (and honest customer feedback) of the many online review sites was considered technically difficult or overly expensive. Until the luxury hotel brand Dorchester Collection did just that, creating a custom AI analytics system that is essentially a giant focus group operating continuously in real time. The system was able to evaluate nearly 7,500 guest reviews from 28 hotels across 10 brands and deliver its findings in a 30-minute video.

As these examples illustrate, the path to memorable customer experience in many industries is determined by how effectively brands leverage their existing customer data into new forms of personalization or, in some cases, entirely new technical capabilities. If CX is on your radar, it’s time to get to work with AI. 

Hacks and security breaches at global companies grab headlines because they’re sensational. A giant organization being held hostage by mysterious hackers is a thrilling, albeit frightening, story. But there’s a lot to be learned from more obscure, outlier cases as well.

Every time we download a new app, log-into a new website, or link personal information to a new device, we create untold opportunities for hackers to steal our information. The following round-up includes some of the more bizarre hacks that have occurred in recent years, and while they are funny, they’re also telling of the unintended consequences of ubiquitous connectivity.

1. Hackers broke into a high-roller database through a fish tank in a casino lobby. You’ve heard that the Internet of Things (IoT) is wildly insecure, and this story more than proves it. When cybercriminals wanted to access a casino’s high-roller database, they hacked into a smart thermometer connected to a fish tank in the lobby. The thermometer was linked to the casino’s Internet connection, and once the hackers were on the network, they were able to steal details on the casino’s VIPs. 
   
2. Teenager used social engineering to hack a former CIA director. The CIA is supposed to be one of the most sophisticated intelligence agencies in the world. But in 2015, a teenager hacked then-CIA director John Brennan’s AOL email. The youthful hacker and his co-conspirators found Brennan’s Verizon phone number, called the company’s customer service line, and persuaded employees that one of them was a staff technician so they could collect information about the intelligence professional’s account. They then used that data to break into his email account, where they found a number of sensitive government documents, including Brennan’s security clearance application.
   
3. British prankster posed as Trump administration officials via email. Brennan wasn’t the only government figure with a compromised email account. Last year, a British prankster hacked the email accounts of several top White House officials and exchanged pleasantries – and barbs – with the likes of former communications director Anthony Scaramucci and the U.S. Ambassador to Russia Jon Huntsman. The hacker pretended to be former chief of staff Reince Priebus and stoked animosity between the latter and Scaramucci. He also posed as the president’s son-in-law, Jared Kushner, and even as Eric Trump. While the hack was apparently intended as nothing more than a joke, it demonstrated how vulnerable government accounts are to malicious actors.
   
4. Cybercriminals hacked accounts on a food delivery app and ordered hundreds of dollars’ worth of take-out food and adult beverages. In 2016, hackers broke into the database of a popular meal delivery app and began using legitimate customer accounts to order themselves tasty dinners and even bottles of alcohol. The app profiles were linked to customers’ bank accounts for ease of ordering, so the cybercriminals simply updated the delivery addresses and dined out on their victims’ dimes. In some cases, the victims didn’t know what had happened until they received a message regarding “their” orders or checked their bank statements. Some orders included several hundred dollars’ worth of food and drinks.
   
5. Marathon runner cheated during a race, and her own wearable device betrayed her secret. A woman who ran a half-marathon in Ft. Lauderdale, Fl., took a short-cut to finish the race, reportedly because she wasn’t feeling well. An understandable decision – except that she altered her running data to appear as those she had won second place. However, a keen-eyed running enthusiast expressed skepticism at the outset and used images of the running watch she was wearing that day to prove she had run fewer miles than she claimed.
   
6. Virtual keyboard company forgot to secure its user database. In another case of unintended self-sabotage, a virtual keyboard company neglected to password protect its data and exposed 577 gigabytes of sensitive user information. More than 31 million users’ data became vulnerable due to the breach, including 6.4 million records containing data from users contacts. In all, the breach exposed more than 373 million records that had been scraped from users’ phones or synced from a linked Google account.
   
7. Anonymous dismantled a fifth of the dark web. The hacker group known as Anonymous compromised roughly 10,600 dark web sites hosted via Tor software, a platform commonly used on the dark web. Many people would view the hack as a public service, as the Anonymous-affiliated hackers said more than half the data on the targeted servers involved child pornography. Nonetheless, the scope of the hack proves just how vulnerable the web is.
   
8. Hack of adult online community outs swingers. A website that proclaimed to be the world’s largest community for sex and swinging failed to secure the personal data of 400 million user accounts. Exploiting the site’s out-of-date and lax data protection measures, hackers were able to access personal information such as users’ IP addresses, emails, and log-in credentials. The leak was especially jarring for former users who believed they had deleted their accounts, only to find that while their profiles weren’t live, they hadn’t yet been wiped from the database.

These breaches are a little more off-the-wall than, say, the Equifax breach or the Sony and WannaCry attacks. But they’re worth heeding because they drive home the fact that more often than not, our data is at risk of being exposed.

As we integrate more IoT devices into our homes and depend on wearables and other technology, we’re generating incredible amounts of data. Without the right security measures in place, all of that information puts us at risk having our identities stolen and our most personal data revealed. It’s worth remembering that even as tech helps connect us in new and innovative ways, it also forces us to take greater responsibility for our digital lives. 

News about blockchain itself can go unnoticed given the public interest in cryptocurrencies like Bitcoin and Ethereum. After all, stories about buying pizzas with Bitcoin are flashier than an analysis of how blockchain might improve the security and portability of medical records. But if you’re paying close attention, there have been many significant announcements of blockchain-based business initiatives at some of the world’s largest companies. They just get drowned out by the crypto craze.

Entefy wanted to discover just how widespread the use of blockchain technologies is around the globe. We decided to use the 30 companies in the Dow Jones index as proxies for the world’s key commercial sectors. The question we wanted to answer: What can we predict about the future of blockchain from the current blockchain strategies of the world’s largest companies? 

We found some surprises along the way. Naturally, many of the most sophisticated blockchain adopters come from the technology and finance sectors. However, the novelty and innovation of blockchain investments outside of those verticals was most surprising. 

We’ll start with highlights of some high-profile yet little-known blockchain initiatives at Dow component companies. Further below, we have shared the complete findings as a quick reference guide. 

Walmart goes all-in on blockchain 

The retail behemoth appears to be going all-in on blockchain technology. It has filed patents for distributed delivery system applications and, more recently, a concept for enabling customer resales using blockchain tracking. 

The resale concept would allow shoppers to register their purchases on the Walmart blockchain and then use the verified data to resell those products down the line. So, presumably, if someone purchased a piece of furniture from Walmart, then decided to list the item online when they moved a year or two later, the product and purchase information would be easily accessible and verifiable by potential buyers. 

If Walmart is successful in developing a blockchain-based delivery system, the program could facilitate not only location tracking but monitoring of factors such as environmental considerations as well. 

But those aren’t the only ways Walmart is incorporating blockchain into its business. The company is also exploring the use of blockchain for improving food tracking, which could prevent or reduce the impact of food safety crises such as the recurring e.coli outbreaks in the U.S. 

Disney imagines a blockchain future 

The Imagineers at Disney are responsible for Dragonchain, a blockchain protocol designed to be more secure than systems using the Ethereum platform. Although the project began in 2014 as a means of creating an in-house asset management system, Disney released it as an open source initiative two years later. 

Today, the one-time Disney employees who built a non-profit around the technology are looking to turn the platform into a business and make it available as a turn-key product to other companies.  

UnitedHealth Group seeks to reduce its data collection and maintenance costs 

Work is already underway to design blockchain systems that overhaul existing medical records systems. Current records management processes are often insecure and inefficient, contributing to physician burnout and patient frustration. But UnitedHealth is working with several other healthcare providers to reduce costs and strengthen the quality of their data via blockchain. 

As of 2017, the healthcare industry was spending $2.1 billion a year on data collection and maintenance. UnitedHealth and its partners aim to lower those expenses, streamline data flows, and decrease redundancies, beginning with testing blockchain applications for improving provider demographic data. They’ll also use their pilot program to explore options for revamping data input and sharing in other areas. 

Coca-Cola leads blockchain initiative to reduce forced labor 

Numerous blockchain applications in government have been identified, but Coca-Cola has carved out a novel approach to a grave human rights issue. The U.S. State Department and Coca-Cola announced earlier this year that they will use blockchain to reduce forced labor and create safer circumstances for sugarcane workers around the world. 

The idea is to use distributed ledger technology to record workers’ employment information and contracts, which could help insulate them from exploitive practices. The issue they’re tackling is a significant one, as 24.9 million people suffer under forced labor situations. If the project is successful at preventing labor abuses, it could inspire blockchain applications for other humanitarian issues. 

Verizon explores new digital security approaches 

The global communications and technology company has announced plans to use blockchain for data security. Verizon partnered with an Estonian company that created a blockchain solution that secures information right at its source. In other words, rather than information being added to the blockchain and transferred between relevant locations, an enterprise’s data is hashed within its system, and the hashes are then transmitted to other programs. Blockchain is often discussed as a potential salve for growing cybersecurity concerns, and Verizon’s initiatives could become a model for other enterprise companies. 

Blockchain projects of the Dow Jones 30

Notes on this research: Where a company has announced multiple blockchain projects, our team has selected the most innovative or noteworthy. Links to sources are included for reference. Companies tagged “No/Unclear” may be pursuing blockchain initiatives but have not officially released details.

3M (MMM): Yes

Structure of project: Partnership

Functional area: Reducing counterfeit pharmaceuticals

Description: 3M used the Microsoft Azure blockchain platform to develop a smart labeling system that would reduce counterfeit drugs. By having drug labels scanned and recorded on the blockchain at every point in the manufacturing and distribution processes, 3M aims to limit instances of counterfeit drugs on the market. Not only does this decrease drug companies’ costs, it also reassures consumers that when they receive their prescriptions, they’re getting the right medication.  

American Express (AXP): Yes

Structure of project: Partnership

Functional area: Test program for delivering cardholder rewards via blockchain and smart contracts

Description: AmEx partnered with the wholesaler Boxed to test a loyalty rewards program run on blockchain and smart contracts. Boxed will be able to make loyalty offers and fulfill them over smart contracts, while AmEx will receive anonymized data about which offers were made and accepted. If the project is a success, it could implement similar processes for other merchant partners. American Express also launched blockchain-based B2B payments via Ripple for U.S. to U.K. transactions in late 2017. 

Apple (AAPL): Yes

Structure of project: Internally developed 

Functional area: Timestamp verification 

Description: Apple filed a patent application in late 2017 indicating that it is exploring the use of blockchain and public key infrastructure technology for certifying timestamps.

Boeing (BA): Yes

Structure of project: Internally developed 

Functional area: Navigation systems 

Description: Boeing filed a patent in 2016 describing its work on an “on-board backup and anti-spoofing” system that would also serve as a back-up navigation program when GPS access lags or becomes unreliable.

Caterpillar (CAT): No/Unclear

Structure of project: N/A 

Functional area: N/A

Description: N/A

Chevron (CVX): No/Unclear

Structure of project: N/A 

Functional area: N/A

Description: N/A

Cisco Systems (CSCO): Yes

Structure of project: Internally developed 

Functional area: Confidential group messaging

Description: In March 2018, Cisco filed a patent describing a group messaging concept. The platform would be secured by cryptographic keys, so users could assume a high degree of safety for files, chats, and membership records protected by the system.   

Coca-Cola (KO): Yes

Structure of project: Partnership 

Functional area: Reducing forced labor

Description: Coca-Cola is working with the State Department to better track sugar cane worker contracts in developing countries and reduce instances of forced labor.  

Dow DuPont (DWDP): Yes

Structure of project: Partnership 

Functional area: Supply chain management

Description: Dow DuPont is reportedly also working with IBM and Maersk as part of the joint venture’s blockchain pilot program.  

Exxon Mobil (XOM): No/Unclear

Structure of project: N/A 

Functional area: N/A

Description: N/A

General Electric (GE): Yes

Structure of project: Internally developed 

Functional area: Energy marketplace

Description: An engineer at GE Global Research said the company is evaluating potential applications for blockchain in developing a marketplace that connects customers and renewable energy producers.

Note: GE is scheduled to be removed from the Dow Jones 30 index.

Goldman Sachs Group (GS): Yes

Structure of project: Internally developed   

Functional area: Consumer education and trading

Description: Goldman Sachs is doing its part to educate consumers about blockchain technology and its potential uses via an interactive section of its site dedicated to the topic. The company also announced that it is establishing a Bitcoin trading operation, a landmark move for the financial services industry.

Home Depot (HD): Yes

Structure of project: Partnership  

Functional area: Supply chains

Description: Home Depot is one of several corporations sponsoring research at Auburn University into how distributed ledger technology can improve supply chain functions.  

IBM (IBM): Yes

Structure of project: Internally developed, partnerships   

Functional area: Food security, supply chain, banking and finance, travel, media, healthcare, energy

Description: IBM has been developing blockchain applications for a wide array of industries and has partnered with other organizations to put the technology to work in the real world. Walmart used IBM’s Food Trust blockchain program in its food tracking efforts, and IBM recently announced a joint venture with Maersk aimed at improving cross-border supply chain functions using blockchain.

Intel (INTC): Yes

Structure of project: Internally developed   

Functional area: Cryptocurrency mining and genetic sequencing

Description: Since 2016, Intel has been experimenting with accelerating cryptocurrency mining to make the process more energy-efficient and cost-effective. Intel researchers are also looking at how blockchain mining platforms could be used in genetic sequencing.

Johnson & Johnson (JNJ): Yes

Structure of project: Partnership 

Functional area: Medical prescription tracking

Description: Johnson & Johnson is working with Intel, McKesson Corp., and other partners to test the use of blockchain in alleviating the opioid epidemic. The companies will use simulated data to see whether secure blockchain records can prevent “double doctoring,” in which patients visit multiple doctors to obtain opioid prescriptions. In theory, blockchain records will help physicians track which medications patients have already been prescribed, limiting access to the problem drugs. 

JPMorgan Chase (JPM): Yes

Structure of project: Partnership 

Functional area: Payment processing

Description: JPMorgan Chase announced in October 2017 that it had launched a blockchain-based payments processing network that would facilitate faster, more secure transactions. The company worked with Australia and New Zealand Banking Group and Royal Bank of Canada on the project.

McDonald’s (MCD): No/Unclear

Structure of project: N/A   

Functional area: N/A

Description: N/A

Merck & Co. (MRK): Yes

Structure of project: Partnership 

Functional area: Smart contracts

Description: Merck joined the Enterprise Ethereum Alliance, a coalition of corporations interested in using Ethereum’s blockchain for smart contracts. The Ethereum blockchain is well-known for being the technology underlying ether, a cryptocurrency. However, the open source blockchain has drawn attention from companies keen to use it for smart contracts, which allow parties to create secure, transparent, and verifiable business agreements.

Microsoft (MSFT): Yes

Structure of project: Internally developed 

Functional area: Digital identity security

Description: In addition to the Azure blockchain platform, Microsoft is developing blockchain solutions for digital identity security. It envisions users reclaiming ownership of their online information via encrypted data hubs and decentralized identifiers.

Nike (NKE): No/Unclear

Structure of project: N/A 

Functional area: N/A

Description: N/A

Pfizer (PFE): Yes

Structure of project: Partnership 

Functional area: Prescription tracking

Description: Like 3M, Pfizer is experimenting with using blockchain to track prescription medications and stem the flow of counterfeit drugs. The company partnered with Genentech and other organizations on the MediLedger Project to create blockchain solutions to supply chain issues.

Procter & Gamble (PG): Yes

Structure of project: Partnership 

Functional area: Supply chain efficiency

Description: Procter & Gamble is exploring the blockchain platform being developed by Danish shipping giant Maersk for use in streamlining its global supply chain.

Travelers Companies (TRV): No/Unclear

Structure of project: N/A 

Functional area: N/A

Description: N/A

UnitedHealth Group (UNH): Yes

Structure of project: Partnership

Functional area: Data management

Description: UnitedHealth Group is working with Humana, Quest Diagnostics, and MultiPlan on a blockchain pilot program aimed at improving data collection and management among healthcare providers.

United Technologies (UTX): Yes

Structure of project: N/A 

Functional area: N/A

Description: United Technologies has stated that it is “actively exploring uses for blockchain technology as it has the potential to drive positive profound business change for UTC.”

Verizon Communications (VZ): Yes

Structure of project: Partnership 

Functional area: Data security

Description: Verizon is working with a European startup to deploy blockchain security solutions by hashing information within an enterprise system rather than transferring it across locations.

Visa (V): Yes

Structure of project: Partnership   

Functional area: Cross-border business payments

Description: Visa worked with a blockchain startup to develop a cross-border payment system for B2B transactions. As of November 2017, Commerce Bank and several Asian banks were involved with the project. Visa is expected to launch a commercial service in the middle of this year.

Walmart (WMT): Yes

Structure of project: Internally developed   

Functional area: Tracking retail purchases for resale marketplace

Description: Walmart filed a patent in November 2017 related to using blockchain technology to track and verify purchases that could be resold at a later date. The company has also worked with IBM to test food tracking via blockchain for improved food security.  

Walt Disney Co. (DIS): Yes

Structure of project: Internally developed   

Functional area: Asset management and data privacy

Description: Developers at Disney built the Dragonchain blockchain for internal asset management, but the technology was eventually made open source. Former Disney workers have now built a non-profit and company around Dragonchain and are looking to make it more widely available.

This year will be a landmark moment for emerging technology, thanks to the initial rollouts of hyper-fast 5G wireless networks. U.S. wireless companies are already rolling out 5G connectivity in select markets, laying the groundwork for low-latency Internet connectivity up to 100 times faster than today’s current 4G/LTE networks. The technology is expected to spur significant changes to diverse areas including autonomous vehicles, smart cities, and even economic growth.

5G also heralds the beginning of fast, ubiquitous connectivity, allowing apps, networks, and smart appliances to fade into the background even as their capabilities become greater and more impactful. We’re several years away from that point, as just a handful of cities will experience 5G this year. But as its availability becomes more widespread, 5G will influence everything from global prosperity to AI to how we think about cybersecurity.

The age of Internet-enabled prosperity

Internet access strongly correlates with a country’s competitiveness, so much so that the World Economic Forum called broadband connectivity a “silver bullet” for economic development. The organization asserts that the 5G-powered Internet will literally change the world by bringing unprecedented competitiveness and technological readiness to developing countries.

As high-speed Internet reaches more developing economies, it creates opportunities for innovation, entrepreneurship, and education. It also encourages infrastructure investments that bring connectivity to even greater numbers of people. Those changes make a real impact, as evidenced by the World Bank’s estimate that a 10% increase in high-speed broadband penetration can lift a country’s economic growth by 1.38%. 

Ubiquitous Internet access will drive change across societies, allowing for the creation of a new generation of mobile apps and web-based services in everything from health to finance to education. The more people have access to basic healthcare information, or can learn about personal financial management, or are able to improve themselves through high-quality educational materials, the greater the potential for quality of life to improve.

The cybersecurity challenges of 5G

For all its potential, 5G also carries with it significant concerns about cybersecurity. Existing wireless infrastructure isn’t nearly as secure as it needs to be, largely because few companies accurately foresaw how rapidly the digital landscape would change. Trying to patch security holes one at a time is not a long-term solution, particularly as 5G multiplies the number of systems and devices connected to wireless networks. As more and more information is captured and stored in the cloud, security threats become more dire. Perhaps it’s unsurprising that President Trump mulled the idea of nationalizing 5G technology to cope with the problem (though the idea was quickly scrapped).

Faster Internet and increased cybersecurity facilitate a more robust and reliable Internet of Things (IoT). Yet it also means more devices connected to the web that are linked to personal data, creating more targets for data-hungry cybercriminals. Although smart devices are becoming more sophisticated, the IoT remains quite insecure, and 5G will only make it more vulnerable to serious breaches.

In a security white paper, researchers at the University of Surrey noted that IoT devices poses several challenges to 5G ubiquity. One is that older, redundant devices can cause network interference that is “the radio equivalent of space junk or seas full of plastic bags.” But embedding the option to remotely deactivate redundant devices increases the chances of Denial of Service attacks if the appropriate security protocols aren’t in place.

Another 5G data security challenge emerges from the sheer volume of devices that will be online. Gartner predicted that there will be more than 20 billion “connected things” by 2020. Many IoT devices are designed to have long life spans, making it difficult in some cases to implement regular security upgrades over their entire life cycle.

Protecting 5G networks against cyberthreats will be a monumental and ongoing effort that will need to address factors like app security, infrastructure protocols, and supply chain management.

The view from 5G

Even with the new classes of cybersecurity concerns that 5G raises, expect the technology to accelerate the deployment of advanced capabilities and emerging technologies. Here are four markets where 5G is already making an impact:

Transportation. One obvious beneficiary of faster Internet connectivity is the self-driving car sector. As 5G becomes more widely available, cars will be able to communicate directly with one another quickly enough to make autonomous vehicles viable at a mass scale.

However, other areas of the automotive industry like public transportation and delivery companies stand to benefit as well. Instead of having to follow the same routes regardless of traffic, bus lines could adjust in real-time based on current conditions. Commercial trucking companies would also benefit from 5G-enabled mechanisms. They could update their routes to respond to traffic patterns or weather events as well, and the responsive nature of their processes would allow them to provide more accurate delivery estimates to their clients.

Smart cities. 5G technology is the gateway to massive improvements in smart city development. Lightning-speed communication among traffic sensors, smart cars, smartphones, and energy grids represent new channels to improvement a city’s efficiency, cost-effectiveness, and public safety. Traffic lights that change based on traffic patterns instead of fixed schedules, sensors that collect information about public health trends, and energy grids that adjust according to usage are just a sampling of the systems that will support smart cities. Accenture estimated that 5G-related infrastructure investments will lead to the creation of 3 million jobs and add $500 billion to GDP growth.

Agriculture. Farmers have been pioneering the use of sensors and other IoT devices in monitoring their crops to optimize water and nutrient levels and scan for signs of disease. It has been estimated that by 2050, farms will produce approximately 1.4 million data points daily, each with the potential to contribute to growing more and higher-quality food.

5G also provides the connectivity backbone for IoT agricultural platforms that track livestock and automatically fertilize crops based on their health and environments. All of these functions are vital to ensuring high yields and profitability.

Media. 5G will change the game for media consumption and advertising, creating ever more overlap between the two areas. Companies that can track customer behaviors in real-time and capture location data will be able to serve hyper-relevant offers to consumers. The lines between entertainment and shopping will continue to blur as media platforms integrate purchase options and advertising experiences into their platforms.

In one word, “integration” neatly sums up the 5G future. The faster and better Internet connectivity becomes, the more easily and reliably billions—climbing to trillions—of devices will be able to communicate with one another instantly, continuously, and in real-time. That means we’ll be able to move seamlessly from point to point (both in the physical and digital worlds) with our devices and our data carrying us wherever we need to go.