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.

There is already enough food to feed everyone on Earth, with agricultural producers yielding 17% more food now than they did just three decades ago. Yet 925 million people worldwide suffer from a lack of food security, including 42.2 million in the United States alone. Several factors contribute to the problem, including poor storage and sanitation systems, low crop yields, and political upheaval. As the global population marches ever higher, the Food and Agriculture Organization predicts that food production will need to increase 70% to feed the world by 2050. 

How will the agriculture industry keep pace? Through cutting-edge technologies like artificial intelligence, IoT, and blockchain, of course.

IoT in the field

When people hear the term Internet of Things (IoT), they often think of smart homes and self-starting appliances. Or perhaps they think about how at present, the Internet of Things is still wildly insecure. But from an agricultural production perspective, the Internet of Things holds real potential for increasing crop yields and reducing losses.

IoT sensors installed in the field enable agricultural companies to monitor their crops in real time. The sensors capture data on a range of metrics and send back information that enables producers to optimize their growing processes. They can monitor nutrient and water levels in the soil and adjust them as needed to get the greatest number of healthy, saleable crops. Imaging programs allow them to see exactly what’s happening across different fields and intervene immediately if they identify diseased plants. 

One company predicts that by 2050, farms will produce approximately 4.1 million data points each day, and every one of them can be used to grow more and better food. Already, smart farming devices are shaping the future of agriculture by helping farmers increase yields and lower their production expenses. On average, the farms included in one series of studies saw a 1.75% jump in yields and a decrease in energy expenses of up to $13 per acre. 

Smart robots and predictive analytics 

Artificial intelligence (AI) complements agricultural IoT devices and further improves the growing and selling processes via predictive analytics. These programs can help farmers determine which crops to grow and anticipate potential threats by combining historical information about weather patterns and crop performance with real-time data. The more information that’s collected, the more precise the insights producers will be able to glean about what’s happening with their crops and where they can optimize conditions. 

Smart robots will likely come into play as well. Already, companies are developing robots that can analyze crops in the field to not only identify disease indicators but to prune away weak plants to give strong ones the space and resources needed to thrive. One U.S.-based company expects that smart systems, such as the artificial intelligence programs it uses to capture images of tomatoes grown in its greenhouses, will ultimately boost its yields by 20%. 

Blockchain equals better business 

Blockchain technology is revolutionary because of its implications for creating secure, transparent records. This ability will be incredibly valuable for the agriculture industry, where it can be used to establish smart contracts and track food from its origins to grocery stores. An American company recently conducted a massive sale of 60,000 pounds of soybeans to a Chinese buyer all on blockchain. Agricultural deals of this scale often require a great deal of back-and-forth, with multiple agents and decisionmakers involved in the trade. When such sales are conducted using pen and paper, the risk of mistakes and logistical delays is high. 

Blockchain simplifies the process because everyone has access to the same information, such as receipts, letters of credit, and necessary certificates, all of which are stored clearly and accurately for every party to see. The businesses involved in the deal reported a fivefold reduction in time spent on the logistics of the agricultural commodity trade, which they claimed was the first to be completed via blockchain. 

Blockchain may also help curb future food safety crises, such as the e.coli outbreak that caused people in 25 states to become sick after eating contaminated lettuce. Scientists struggled for at least two months to identify where the diseased crops came from, highlighting the need for better supply chain documentation and management throughout the industry. If agricultural growers and their supply chain partners tracked crops on a blockchain, they could easily identify where different crops originated and who was involved in growing and transporting them. 

A major American food seller found that blockchain programs allowed it to trace a piece of the produce’s origins in just over two seconds, compared to the nearly seven days it takes via more manual processes. Not only does the instantaneous nature of blockchain appeal to consumers who are increasingly concerned about where their food comes from, it has the potential to minimize and even halt widespread contamination outbreaks. From a business perspective, this means pinpointing the source and acting precisely to root out the problem, as opposed to fumbling through disorganized data and taking losses on food that was unnecessarily discarded or unsold as a result of the outbreak. 

The future of agriculture 

Agriculture companies have proven that they’re up to the task of producing food for most of the world. Growers and their partners must do even better if they’re to reduce food insecurity and keep pace with population growth. The time to invest in smart technologies and precision agriculture has arrived. Companies that use IoT devices, AI systems, and blockchain stand to improve their yields, reduce costs, and enhance their profits. The business case for tech-enabled agriculture is clear; now it’s a matter of broader adoption and implementation. 

Productivity, efficiency, and growth all measure different aspects of something fairly straightforward: the transformation of inputs (like time or resources) into outputs (ideas, goods, services, solutions). A simple concept with profound implications for individuals and organizations alike.

Digital technology has an important role in personal and organizational productivity. From instantaneous messaging between colleagues and friends to automated Human Resources systems, digital technologies are central to our lives, at home or at work. But there is room for tremendous improvement in the effectiveness of many of these technologies.

In this quick video, Entefy’s Co-Founder Brienne Ghafourifar presents an overview of 4 key challenges faced by individuals and organizations alike in their quest for productivity, efficiency, and growth.

In the U.S., 98% of medical students take a pledge commonly referred to as the Hippocratic oath. The specific pledges vary by medical school and bear little resemblance to the 2,500-year-old oath attributed to the Greek physician Hippocrates. Modern pledges recognize the unique role doctors play in their patients’ lives and delineate a code of ethics to guide physicians’ actions. One widely used modern oath states:

“I will not permit considerations of age, disease or disability, creed, ethnic origin, gender, nationality, political affiliation, race, sexual orientation, social standing, or any other factor to intervene between my duty and my patient.”

This clause is striking for its relevance to a different set of fields that today are still in their infancy: AI, machine learning, and data science. Data scientists are technical professionals who use machine learning and other techniques to extract knowledge from datasets. With AI systems already at work in practically every area of life, from medicine to criminal justice to surveillance, data scientists are key gatekeepers to the data powering the systems and solutions shaping daily life.

So it’s perhaps not surprising that members of the data science community have proposed an algorithm-focused version of a Hippocratic oath. “We have to empower the people working on technology to say ‘Hold on, this isn’t right,’” said DJ Patil, the U.S. chief data scientist under President Obama. The group’s 20 core principles include ideas like “Bias will exist. Measure it. Plan for it.” and “Exercise ethical imagination.” The full oath is posted to GitHub.

The need for professional responsibility in the field of data science can be seen in some very high-profile cases of algorithms exhibiting biased behavior resulting from the data used in their training. The examples that follow here add more weight to the argument that ethical AI systems are not just beneficial, but essential.

1.     Data challenges in predictive policing

AI-powered predictive policing systems are already in use in cities including Atlanta and Los Angeles. These systems leverage historic demographic, economic, and crime data to predict specific locations where crime is likely to occur. So far so good. The ethical challenges of these systems became clear in a study of one popular crime prediction tool. PredPol, the predictive policing system developed by the Los Angeles police department in conjunction with university researchers, was shown to worsen the already problematic feedback loop present in policing and arrests in certain neighborhoods. “If predictive policing means some individuals are going to have more police involvement in their life, there needs to be a minimum of transparency. Until they do that, the public should have no confidence that the inputs and algorithms are a sound basis to predict anything,” said one attorney from the Electronic Frontier Foundation.

2.     Unfair credit scoring and lending

Operating on the premise that “all data is credit data,” the financial services industry is designing machine learning systems that can determine creditworthiness using not only traditional credit-worthiness data, but social media profiles, browsing behaviors, and purchase histories. The goal on the part of a bank or other lender is to reduce risk by identifying individuals or businesses most likely to default. Research into the results of these systems has identified cases of bias like, for example, two businesses of similar creditworthiness will receive different scores due to the neighborhood the business is located in.

3.     Biases introduced into natural language AI

The artificial intelligence technologies of natural language processing and computer vision are what give computer systems digital eyes, ears, and voices. Keeping human bias out of those systems is proving to be challenging. One Princeton study into AI systems that leverage information found online demonstrated that the same biases people exhibit make their way into AI algorithms via the systems’ use of Internet content. The researchers observed, “Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language—the same sort of language humans are exposed to every day.” This is significant because these are the same datasets often used to train machine learning systems used in other products and systems.

4.     Limited effectiveness of healthcare diagnosis

There is limitless potential for AI-powered healthcare systems to improve patients’ lives. Entefy has written extensively on the topic, including this analysis of 9 paths to AI-powered affordable healthcare. The ethical AI considerations in the healthcare industry emerge from the data that’s available to train machine learning systems. That data has a legacy of biases tied to variability in the general population’s access to and quality of healthcare. Data from past clinical trials, for instance, is likely to be far less diverse than the face of today’s patient population. Said one researcher, “At its core, this is not a problem with AI, but a broader problem with medical research and healthcare inequalities as a whole. But if these biases aren’t accounted for in future technological models, we will continue to build an even more uneven healthcare system than what we have today.”

5.     Impaired judgement in criminal justice sentencing

Advanced artificial intelligence systems are at work in courtrooms performing tasks like supporting judges in bail hearings and sentencing. One study of algorithmic risk assessment in criminal sentencing revealed how much more work is needed to remove bias from some of the systems supporting the wheels of justice. Examining the risk scores of more than 7,000 people arrested in Broward County, Florida, the study concluded that the system was not only inaccurate but plagued with biases. For instance, it was only 20% accurate in predicting future violent crimes and twice as likely to inaccurately flag African-American defendants as likely to commit future crimes. Yet these systems contribute to sentencing and parole decisions.

Entefy previously examined specific action steps for developing ethical AI that companies can use to help ensure the creation of unbiased automation. 

With a technology as impactful as machine learning, it can be difficult to avoid hyperbole. Sure, billions of dollars in investment are pouring into ML projects. Yes, machine learning is a centerpiece of digital transformation strategies. And, to be certain, machine learning is often what people are talking about when they use the umbrella term “AI.” So it’s worth taking the time to look at real-world ML capabilities being developed and deployed at digitally nimble companies around the globe.

Entefy published two previous articles covering the machine learning revolution, which you can access here and here. We continue this global survey below with a look at how 8 more industries are making use of machine learning technology.

Pharmaceuticals & Life Sciences

Wherever you fall on the death disruption debate, we can all agree that aging is a challenging experience. Even if you don’t aspire to immortality, you likely recognize that increased joint pain and susceptibility to illness and injury will erode your quality of life. But deep learning may be able to slow the aging process. Scientists are now using the technology to identify biomarkers associated with aging. Soon enough, a simple blood test could tell you which parts of your body are showing signs of wear and tear, and your doctor could help you mitigate, and perhaps reverse, those affects through lifestyle recommendations and medication.

Food

Up to 40% of a grocer’s revenue comes from sales of fresh produce. So, to say that maintaining product quality is important is something of an understatement. But doing so is easier said than done. Grocers are at the whims of their supply chains and consumer fickleness. Keeping their shelves stocked and their products fresh is a delicate balancing act.

But grocers are discovering that machine learning is the secret to smarter fresh-food replenishment. They can train machine learning programs on historical datasets and input data about promotions and store hours as well, then use the analyses to gauge how much of each product to order and display. Machine learning systems can also collect information about weather forecasts, public holidays, order quantity parameters, and other contextual information. They then issue a recommended order every 24 hours so that the grocer always has the appropriate products in the appropriate amounts in stock. 

Businesses that have implemented machine learning in their replenishment workflows reduce their out-of-stock rates by up to 80%, along with up to 9% in gross-margin increases.

Media & Entertainment

Machine learning allows media companies to make their content more accessible to consumers through automatic captioning systems. Since implementing an automatic captioning program, YouTube has enabled 1,000,000 functionally-deaf Americans and 8,000,000 hearing impaired to watch and enjoy its videos. As of 2017, its machine learning programs had become sophisticated enough to include captions for common non-speech audio, such as laughter and music, creating an even more complete experience.

Information Technology

Although machine learning is generating unprecedented business insights, many organizations have failed to invest adequately in AI systems. For instance, McKinsey found that “the EU public sector and healthcare have captured less than 30 percent of the potential value” of Big Data and analytics. Organizations that want to avoid a similar mistake will need to ramp up their data science abilities – but so will workers who want to stay competitive. By 2020, there will be more than 2.7 million data science jobs, and the demand for workers who understand and can work with machine learning technology will only grow from there.

Law

Deep learning applications are especially impressive in the legal sector due to the nature of the language these programs must parse. Legal phrasing can be complex and difficult to decipher, yet deep learning systems are already capable of analyzing tens of thousands of vital documents. When legal teams needed to dissect contract clauses that upset their or their client’s business and invoicing processes, they once had to manually review stacks of rigorously prepared documents. Now, they can feed them into a program that works far faster than any lawyer and can pick out important phrases for further analysis by the legal team.

Insurance

Improving risk prediction and underwriting is in everyone’s interest, which is why machine learning is such a gift to the insurance industry. In auto insurance, for instance, machine learning algorithms can use customer profiles and real-time driving data to estimate their risk levels. They can then formulate personalized rates based on that information, potentially creating savings for both consumers and insurance companies.

This process may be enhanced by even more in-depth analyses, in which machine learning programs pull in seemingly unrelated social media data to create a more precise profile. The insurance industry could use artificial intelligence to identify which policyholders are gainfully employed and which seem to be in good health. Theoretically, someone who is responsible in those areas of their lives will be a responsible driver as well.

Education

Intelligent tutoring systems (ITS) hold enormous potential for disrupting the classroom and helping students learn. These AI programs serve as virtual tutors, and they adapt their digital lessons based on each child’s strengths and weaknesses. Each time the student completes a task or quiz, a machine learning program processes that information to customize future materials.

By “learning” a user’s unique needs and identifying which types of lessons are most effective for them, the ITS helps the student overcome learning challenges and retain more knowledge. Research indicates that students who use intelligent tutoring systems perform better on tests than their peers who learn via large-group instruction.

For an overview to key concepts in artificial intelligence, check out Entefy’s article Essential AI: A brief introduction to terms and applications.