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. 

Artificial intelligence gets due praise for its power to improve the way we work. Yet that same potential can be used to improve the way we live, even addressing some of our most intractable social issues. Recent developments in AI and other smart technologies are rare good news in one of America’s most urgent and emotionally fraught conversations: gun violence. 

The February 2018 shooting at Marjory Stoneman Douglas High School was the most high-profile incident in recent months, but school shootings have averaged one per week since the beginning of the year. Fear is prevalent, with one Gallup poll finding that 4 in 10 Americans fear becoming a victim of a mass shooting. In the wake of these tragedies, people are desperate for answers and reassurance. Everyone agrees something must be done, but they can’t agree on what. Yet the discussion around gun control and the prevention of mass shootings is highly politicized and emotionally charged. 

Technology, however, is dispassionate. Computer algorithms aren’t prone to uncertainty or emotion and can analyze data, lock weapons remotely, or identify guns before they’re drawn. Because of this, they can provide a steadying neutral influence as we strive to mitigate gun violence. 

While human decisionmaking and intervention are crucial to solving an issue as complex as gun violence, technologies like artificial intelligence and high-powered sensors are being designed to help stem the tide of mass shootings in the U.S. Here is a look at several innovative technologies being employed to combat the gun violence problem. 

From smartphones to smart guns 

After the 2015 San Bernardino shooting spree, former President Barack Obama publicly questioned why we can secure our smartphones using fingerprint biometrics yet hadn’t done the same for guns. It was a high-profile call for smart gun technology that could help prevent mass shootings. 

Theoretically, gun manufacturers could equip weapons with time-stamping and remote control capabilities that allow authorities to track when they’ve been used and shut them down when they’re being used in an attack. Biometric smart locks could inhibit criminal gun use because the devices would be all but useless unless a would-be shooter could fake the owner’s fingerprint or other biological identifiers. 

One company is already developing a smart lock that requires a registered fingerprint or physical key to unlock the gun’s trigger. Importantly, the locking system doesn’t link to an app or other computer software, a design decision intended to mitigate fears that the gun could be hacked. Increased research into this area could lead to the production of guns that are safer for their owners and for the general public. 

Can AI reduce gun violence? 

Smart guns, metal detectors, and panic buttons all have been discussed for catching potential shooters before they have a chance to act and for alerting emergency services immediately after a shooting incident begins. Software known as panic apps are designed to decrease the amount of time it takes for police and emergency workers to get to a school. Digitizing school blueprints can contribute to helping authorities reach victims faster, reducing the death and injury tolls during mass shootings. 

During a shooting, sensors and motion detectors can help law enforcement officers identify where a shooter is in the building. Although the technology is still in its early stages, smart video surveillance has the potential to assist in prevention as well. Researchers are currently studying how to train artificial intelligence algorithms that identify guns on video feeds. They’re feeding the program with movies like “Mission Impossible” and installments of the “James Bond” franchise so it can learn to detect guns not just on high-resolution screens but on grainier images as well. 

AI could play a supporting role as well. Advocates of gun insurance say that AI underwriting algorithms can keep guns safe and affordable for lawful, capable owners while keeping them out of the hands of high-risk individuals. The theory is that AI algorithms could draw on vast data sources to assess a person’s risk before they’re allowed to buy a gun. These data points might include criminal background checks, medical records, employment status, and even online behavioral data to assess their risk profiles. 

People who have clean records and other indicators of trustworthiness and lawfulness would receive lower insurance premiums. Those who have more erratic histories would be required to pay higher premiums, with the highest risk individuals potentially being priced out of the market. For this to work, gun insurance would need to be mandatory in the same way that auto insurance is required. But if implemented, it might bridge the gap between people on either side of the debate and help forge collective progress toward reducing gun violence.  

Preventing mass shootings is a complicated but essential task, and there are many factors that need to be addressed. Fortunately, we live in the most technologically advanced age in history, and we have access to more data every day. Artificial intelligence can help us make sense of that information by identifying trends we can use to create effective solutions. On gun violence and so many other urgent issues, we can leverage technology to find solutions to the most challenging issues of our time. 

The finance and insurance sector represents 7.3% of U.S. GDP, or about $1.4 trillion. The industry’s reach extends into practically every corner of every commercial market, as well as most U.S. households via checking accounts, 401k and IRA investments, and home and auto insurance policies. As the 2008 financial crisis demonstrated, what happens in this sector directly impacts the rest of the economy – and the world. 

For all of that scale, the financial industry has long struggled with challenges like rising regulatory compliance costs and ever-worsening credit card fraud. Yet these perennial problems are neither inevitable nor permanent. For today’s most forward-thinking banks and financial institutions (FIs), advanced artificial intelligence and machine learning solutions have reached a state of maturity where historic dynamics of compliance and risk can finally be turned around in the banks’ favor. 

The costs of compliance 

In the years following the 2008 financial crisis, the U.S. government ratcheted up regulatory requirements. The ostensible goal was to prevent another economic catastrophe, but bolstering the regulatory environment had some unintended negative effects. The Dodd-Frank bill, for example, was designed to create more stability and transparency within the financial system. However, the Dodd-Frank regulations became such a burden for small banks, many of them closed or were absorbed by larger institutions. This had negative repercussions for the small towns and communities many of them served. 

Increased compliance requirements have traditionally been met with increased hiring of compliance personnel. In just a single quarter during 2013, the finance industry added 10,000 jobs in compliance, risk, or regulatory-related functions. Ten thousand new jobs would usually be a cause for celebration, but compliance costs have become a huge operational and competitive burden for banks and other financial institutions. Like in any other regulated industry, such costs are often passed along to customers. 

There’s no end in sight, either. Accenture found that 89% of financial institutions expect their compliance investments to rise over the next two years. As evidence of their strategic importance, 66% of these institutions’ compliance groups report directly to the CEO or Board of Directors. Increasing compliance spending often forces banks to increase prices or to cancel new product development to minimize expenses and avoid potential losses. 

The link between data breaches and credit card fraud 

Compliance costs aren’t the only thing keeping financial executives up at night. Data hacks such as last year’s Equifax breach revealed the personally identifiable information (PII) of at least 145 million Americans. That’s grave news for banks and credit card issuers, because PII is a golden ticket for cybercriminals who want to commit credit card fraud. These records contain people’s names, addresses, birthdates, and even Social Security numbers in some cases. The exact type of data one needs to open a false account. 

Banks and FIs must also be on alert for synthetic identity fraud, a newer scheme that involves using some legitimate PII with fake data to create an account. It’s all the more insidious because oftentimes, cybercriminals will use a child’s PII for this form of identity theft. The logic is that adults might monitor their own credit accounts closely, but what parent thinks to check their 12-year-old’s credit score? Unfortunately, by the time the family catches on, the fraudster may have seriously jeopardized the child’s future financial standing. 

U.S. banks urgently need a solution to fraud mitigation. By one industry estimate, the U.S. sees 39.5% of the world’s payment card fraud losses. Within just the next three years, annual global payment card losses are projected to climb from $22.8 billion to $32.9 billion. 

Advanced AI can mean cost reduction and fraud prevention 

The financial industry’s monetary challenges are substantial on both the compliance and credit card fraud fronts. Credit card use increased by 2.6% this year, and more accounts means risk prevention and fraud detection will be all the more important. But the sheer volume of activity in both areas makes relying solely on human workers a losing strategy. The financial sector can augment their teams with artificial intelligence to reduce costs and lower their losses related to fraud.  

AI-powered automation can alleviate significant operating costs in functions such as customer verification and anti-money laundering (AML) strategies. The banking industry spends an estimated $270 billion a year on compliance in large part due to personnel costs. Rather than frantically hiring more workers, companies can implement technology that works around the clock – and that’s important, because the need for vigilance is constant. Smart processing systems can also scale up as necessary. No matter the volume of accounts created, computer programs can continue analyzing them at the same speed. 

McKinsey predicts that by 2020 AI-optimized fraud detection will grow to a $3 billion market. Artificial intelligence systems can detect suspicious behaviors within milliseconds. A program can learn a cardholder’s typical spending patterns and flag any purchases that fall outside of those. Machine learning systems continuously gather data on users, which enables them to become more and more accurate in identifying potential crimes. 

Banks and FIs should be taking this technology seriously for a number of reasons. The first is that compliance standards aren’t going away. As the industry grows, so does the regulatory scrutiny, and hiring people to meet tough regulation guidelines simply isn’t sustainable. However, training a machine learning system to flag potential fraud and refer it to trained analysts is a workable solution. It’s also worth noting that in addition to losses directly related to fraud, banks also face fines if their processes aren’t stringent enough to catch criminal activity. The federal government fined one U.S. bank $613 million for failing to implement strong AML protocols. 

But banks and FIs have a duty to their customers as well. Those that implement smart compliance and fraud detection systems position themselves to earn customer loyalty and increase their profits. By lowering compliance expenses, they’re able to invest in innovative concepts and improve their existing products. They also have more flexibility in their pricing structures, allowing them to make more competitive offers. 

Sixty-six percent of Americans fear becoming identity theft victims, and people want reassurance that their data and money is safe. Banks that deploy technologies like AI, machine learning, and advanced cybersecurity stand to enhance their value propositions with their customers and boost their competitive positions over the long run. 

New technology enables on-the-fly encryption and privacy controls that persist for the life of a message

PALO ALTO, Calif. June 13, 2018. Entefy Inc. has been issued a patent by the U.S. Patent and Trademark Office (USPTO). Patent No. 9,985,933 describes a “System and method of sending and receiving secret message content over a network.” The newly patented technology enables novel ways of embedding user-controlled privacy protection into messages and their attachments.

This latest Entefy patent is related to the company’s Adaptive Privacy Controls (APC) solution, a game-changer for individuals and organizations requiring message-by-message control over their digital communications and shareable assets. APC works independently of the underlying messaging protocol (e.g. email or text) and persists through forwarding, separating, or even copying file attachments from the original message.

“Email and digital messaging in general have become ubiquitous technologies despite never having solved fundamental issues of privacy, security, and data ownership,” said Entefy’s CEO, Alston Ghafourifar. “Entefy’s latest patent is a game-changer that allows message senders to transmit confidential or otherwise private information with the assurance that only designated recipients will have access. That’s even if a message is forwarded to a new recipient or its attachments are saved locally. It puts you in charge of the information you share with others.”

Entefy recently revealed its suite of machine intelligence solutions in conjunction with the launch of its new website. Today’s news is the latest in a series of patent issuance announcements, including an Entefy patent that enhances intelligent message delivery and a patent covering encrypted group messaging simultaneously across multiple protocols. To date, the company has a combined 48 filed and issued patents in AI, communication, search, blockchain, data privacy, and cybersecurity.

ABOUT ENTEFY

Entefy is a machine intelligence company developing advanced technologies in contextual cognition, computer vision, natural language, audio, time series, and other data intelligence. Entefy’s SaaS and on-premise solutions deliver transformative AI, communication, search, cybersecurity, IoT, and blockchain capabilities—helping people and organizations Discover & Do more in less time. Get started at www.entefy.com.