Founded by Steve Jobs, Ronald Wayne, and Steve Wozniak in 1976, Apple is a global technology corporation specializing in consumer devices, software, and online services. 

Leading retailers - like Walmart, Stop & Shop, and Home Depot - are enhancing their payment and fraud detection systems, using artificial intelligence that learns transaction norms and infers risk from the context of each transaction.

Supply chains contain every material, component, product and packaging for the objects that together compose the world we live in. However, there is an often invisible ingredient to successful supply chains: data. 

The retail industry collects massive amounts of data every day, and this makes its key processes ripe for automation with machine learning. Along with the manufacturing sector, the retail industry likely stands to benefit the most from one particular AI technique in the next few years: machine vision, also known as computer vision.

The retail and eCommerce sectors were among the first to adopt natural language processing (NLP) in the enterprise, particularly by way of chatbots and conversational interfaces. In this article, we cover three ways retailers can use NLP to automate business processes and offer the customer a better experience. We also give examples of AI vendors that offer this technology and describe their products. The NLP capabilities we discuss include:

The business world has been talking about AI for several years now, and it's safe to say that it's reached a certain cultural moment among executives in areas like banking, insurance, and pharma. Government leaders have been much slower to start the conversation around the capabilities of AI, including the possibilities they offer to militaries and the ethical implications of AI when it comes to governance and the legal system.

Artificial intelligence and machine learning have fueled technological innovations in marketing, eCommerce, and several other industries. Many people experience the benefits of AI and ML systems without even knowing it every time they search on Google or click on a song in Spotify.

Gesture-based interfaces are applications that allow users to control devices using hand and other body parts. Today, they are found in devices used in home automation, shopping, consumer electronics, virtual reality and augmented reality gaming, navigation, and driving, among others.

Episode Summary: AI, specifically natural language processing, has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. AI is already being used to help industries that work in the video medium. However, searching for content within videos is more challenging because video is not just voice and sound, it is also a collection of moving and still images on screen. How could AI work to overcome that challenge?

In recent years, artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices. While static pricing keeps prices absolute, dynamic pricing adjusts prices to offer customers different prices based on external factors and their individual buying habits.

In July 2017, The State Council of China released the “New Generation Artificial Intelligence Development Plan," outlining China's strategy to build a US$150 billion Chinese AI industry in a few short years, and to become the leading nation in AI by the year 2030. Other nations followed suit quickly with national AI strategies of their own - with the US trailing behind by nearly two years before developing a semblance of an AI initiative. The proposed 2021 budget for the national security budget in the US is $740 billion - with a billions of dollars being earmarked for AI specifically (learn more: US Public Sector AI Opportunity Report). 

Event Title: Launch of the OECD AI Policy Observatory

Event Host: OECD

This article is based on a presentation given by Emerj CEO Daniel Faggella in Geneva, at the 2019 New Shape Forum: Weapons Governance for the Geneva Disarmament Platform. To learn more about Emerj's AI presentations and speaking, visit our presentations page.

In July of 2018, Daniel Faggella spoke at the Interpol--United Nations (UNICRI) Global Meeting on the Opportunities and Risks of Artificial Intelligence and Robotics for Law Enforcement. It was the very first event on the usage of AI in policing, security and law enforcement by the UN and INTERPOL. 

Event Title: 2019 New Shape Forum: Weapons Governance

Date: September 30, 2019 - October 1, 2019

This is a contributed article by Kristóf Zsolt Szalay. Kristóf is Founder and CTO at Turbine.AI, and holds a PhD in molecular biology and bioinformatics. To inquire about contributed articles from outside experts, contact editorial@emerj.com.

This article is based on a presentation at the 2017 AI Applications Summit conference in Boston, entitled “Artificial Intelligence in the Hospital Setting” (slide deck embedded below) delivered by Emerj CEO Daniel Faggella - updated with recent insights and interviews from Emerj's podcast and AI Opportunity Landscape data.

We see evidence dating back to 2017 that Johnson & Johnson has been regularly publishing about their investments and initiatives related to artificial intelligence. At present, Johnson & Johnson does not seem to boast any mature, deployed applications with the firm itself, but its AI-related investment initiatives indicate their aspirations.

The COVID-19 pandemic has caused incredible disruption in healthcare systems across the world, as well as an immediate demand for innovative solutions to the growing number of coronavirus patients. Many AI vendors are already trying to find ways they can serve this demand through augmenting their machine learning-powered products - from diagnostics interfaces to radiology solutions and everything in between. 

There are numerous AI initiatives in progress across the healthcare industry; some of these are for mental health and well-being. In this article, we offer an overview of how AI is facilitating mental healthcare.

General Electric (GE) was founded in 1889 by J.P. Morgan and Anthony J. Drexel who came together to finance Thomas Edison’s research and merge their companies together. Originally, GE was an industrial and consumer products company but today, more than 130 years later, GE has transformed itself into a multinational, digital industrial corporation ranked as the 33rd largest company in the United States by gross sales in 2020, according to Fortune 500.

The hypothesis is simple:

Equipment breakdowns or downtime is extremely expensive (imagine a train broken down on isolated tracks, hundreds of miles from the nearest depot)
Heavy equipment (engines, wind turbines, manufacturing machines) produce various streams of data (heat, vibration, time-series, etc)
Machine learning could be used to detect "failure patterns" in that data, helping businesses to maintain equipment health more effectively

This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces.

Accenture forecast the Industrial Internet of Things could contribute $10 trillion to the global economy by 2030. The report also suggested that sensors, material tracking mechanisms, 3D printing, automated product design, robotics, and wearables could help manufacturers reduce costs and increase productivity. Predictive asset maintenance could potentially reduce equipment and machinery maintenance costs by up to 30% and result in up to 70% fewer breakdowns.

McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue.

By implementing an immersive virtual-reality environment, some AI applications claim it is possible to test products or retail ideas that have not been brought to on the market, putting them on a virtual shelf to study consumer reactions and behavior to real-time merchandising.

Episode Summary: AI, specifically natural language processing, has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. AI is already being used to help industries that work in the video medium. However, searching for content within videos is more challenging because video is not just voice and sound, it is also a collection of moving and still images on screen. How could AI work to overcome that challenge?

Speech recognition is technology that can recognize spoken words, which can then be converted to text. A subset of speech recognition is voice recognition, which is the technology for identifying a person based on their voice.

As the web grows and we spend more and more time online, moderation becomes a bigger and bigger challenge. Content influences buyers, and businesses eager to gain the trust of those customers are likely to do whatever they can to win.

eMarketer estimates that 62% of global internet users accessed digital video in 2017, and that number is expected to rise to 63.4% by 2020. This may be marginal growth, but, essentially, consumers are expected to spend more time watching video content. 

This article has been sponsored by Iron Mountain, and was written, edited, and published in alignment with Emerj’s sponsored content guidelines. 

Oil and gas companies face many of the same challenges as large banks and established insurance firms when it comes to searching through their backlogs of documents. They want to use the data stored within these documents to make decisions on where to drill and determine whether or not they’re in compliance with laws and regulations. 

Although it's apparent that AI development is slow-moving in the industry, food and beverage companies may be able to use AI for food processing, in particular:

The International Energy Agency’s latest annual gas market report, Gas 2018, estimated that global gas demand could reach more than 4,100 billion cubic meters (bcm) in 2023. This is an increase from 3,740 bcm in 2017. Greater gas demands mean more oil rigs, and the machines on these rigs break down.

There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world.

Emerj serves enterprises to form their AI strategies, and data audits are part of Emerj's framework for identifying high-ROI AI projects. In this article, we'll break down a slice of this framework, walking through some pragmatic steps leaders can take to drive toward industry-leading outcomes in their organization.

What to do when your clients want to cut costs?

Help them cut costs, and be part of a bigger vision beyond cost-cutting.

Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livne, co-founder and CEO of Verbit.ai.

AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms.

IT services firms are doing their best to keep up with the changing landscape as AI begins to dominate the tech conversation. Many IT services firms have recently started branding themselves as AI companies without having the requisite talent to back up their claims. In reality, many of the older IT services companies are struggling to hire PhD graduates in machine learning who would rather use their skillset at global AI firms like Google and Amazon.

Supply chains contain every material, component, product and packaging for the objects that together compose the world we live in. However, there is an often invisible ingredient to successful supply chains: data. 

The National Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation recently released an overview report on the current state of self-driving technology.

Danny Lange heads up the AI efforts at Unity, one of the better-known firms in terms of simulations and computer graphics. They work in several different industries, but this week we speak mostly about automotive.

In this article, we explore the applications of AI software within the automotive industry from production and manufacturing to insurance and transportation. We will discuss the equipment involved in collecting and analyzing data along with the potential value they offer to manufacturers, shared mobility companies, insurers, and drivers.

Although AI has broader applications in the travel and tourism industry, facial recognition kiosks at airports have been one of the most prominent applications in the public discussion about AI. Their promise: to increase security and potentially speed up passenger boarding.