IBM Immersive Insights helps users quickly discover new findings by letting them see and touch their data. Yes, it’s real. Yes, you can use it today.

What can I make with AI right now?

A continuously growing collection of videos demonstrating what AI is good at solving for users today

Introduction

A bit about why we are where we are with AI today.

Predicting, automating, and optimizing time. That’s why you want to do AI.
Rob Thomas, GM for IBM Analytics

For the past 50+ years, AI’s progress was mainly limited by our inability to create faster computers, as well as coming up with a natural way for us to interact with them. Now that we’ve solved those issues well enough for the time being, we’re bumping into two new, but equally daunting challenges that are preventing us from fulfilling our sci-fi fantasies.

First, we can’t get data fast enough because, hey, who knew that’s what we should’ve been working on rather than widgets and album carousels?

The growth rate of worldwide big data revenue is estimated to jump from $49 billion in 2019 to $103 billion by 2027.

International Data Corporation, 2018

There are two solves for anyone facing a data deficit:

1. If historic data is necessary for the accuracy of your model (ex. your model maps weather trends or traffic patterns), it’s going to take you some time to gather and digitize old data files, or to capture, label, and train new ones.
2. If historic data isn’t a factor, or simply doesn’t exist (ex. your model is about making quality recommendations or diagnosing a new disease), then focus on finding new ways to train models using lower quantity, but higher quality, data. This usually requires subject matter experts and data creation services like those provided by Figure 8 and Mechanical Turk to author the data.

“If you look at all the applications where deep learning is successful, you’ll see they’re in domains where we can acquire a lot of data. The problem is not really about finding ways to distribute data, but about making our deep learning systems more efficient and able to work with less data.”

Professor Neil Lawrence in “These are three of the biggest problems facing today’s AI”, The Verge, Oct. 2016

“Do You Have Enough Data For Machine Learning?”, Forbes, Nov 2019.

Coming in at a close second is the lack of AI expertise in the world. First and foremost we need data scientists, but also AI focused domain, legal, ethical, operational, design, management, support, and training maintenance experts.

Demand for data scientists is off the charts. Shortages are present in almost every large U.S. city. Nationally, we have a shortage of 151,717 people with data science skills, with particularly acute shortages in NYC, SF, and LA.

LinkedIn Workforce Report, August 2018

Job and industry statistics by McKinsey, 2018. Retina melting visualization by Hadoop.

Given these two speed bumps, where you see AI growing the most is in the machine learning sector. Machine learning is tool that can actually train AI to solve problems for us–hence, everyone is hyper-focused on writing an awful lot of algorithms these days–even if the data fueling these models isn’t ready yet.

If you combine our lack of data,
+ our lack AI experts,
+ our focus on ML,
then what AI can do best today tends to fall into six categories:

Automation

Conversational interaction

Insights, predictions, recommendations, and support

Optimizing efficiencies

Personalization

Transparency and training

Think of these examples like the AI version of simple machines. Right now we’re focused on getting the simple pieces built, then we’ll be able to combine them to create compound AI machines, like worker-less factories and AI powered nursing homes.

Not a real thing, but you get the point. And it easily could be built.

We’re starting to see attempts at compound AI use cases already that get more wow factor by stringing several types of ML together — like analytics + NLU + sentiment analysis — then combining them with a flashy delivery system like voice recognition, IoT sensors, or AR visualizations.

Just like determining what AI capabilities are growing the fastest, we can use the same approach to figure out which domains are using AI the most — it’s the industries that have the most money that are putting it to use. The big players currently include:

• Banking, financial services and insurance
• Legal and policy compliance
• Sales and marketing
• Manufacturing
• Retail
• Healthcare and life sciences
• Telecommunication
• Energy and utilities
• Government and defense

That said, regardless of how you want to use it or what industry you’re in, if you’re trying to determine whether AI might be a good tool for solving the problem you’re working on, an easy way to find out is to ask yourself…

Could it provide my users

better insights,

with more confidence,

faster than humanly possible?

If you can answer yes to one or more of these points, then it’s worth investigating more closely how AI could benefit your users.

Now let’s take look at real world examples of today’s six AI capabilities.

If you have case studies or capabilities you think should be added, please leave them in the comments section below.

AI Capability 1: Automation

Any small or repetitive tasks your user or business performs should be considered for automation.

Autodesk reduced their customer support costs from $15.00–$200.00 per case to $1.00 per case.

Woodside, Australia’s largest energy company, is becoming a cognitive business with Watson and IBM. Woodside Energy designed a cognitive system with Watson that has digested over 38,000 documents filled with practical engineer experience — something that would take a human almost 5 years to do on their own. Implementing Watson has allowed employees to decrease the amount of time that they spend looking for information, so that the engineers who use the technology can better perform their jobs.

Vodafone UK uses Watson to improve customer satisfaction.

IBM Watson helps Tax Pros maximize tax outcomes with H&R Block. Watson is working alongside H&R Block Tax Pros as they take clients through the tax return process, suggesting key areas where he or she may qualify for deductions and credits.

Kone is connecting, remotely monitoring and optimizing the management of more than 1 million elevators and escalators with Watson IoT.

Powered by Google Cloud, Live Transcribe captions conversations in real-time, supporting over 70 languages and more than 80% of the world’s population.

ViSenze uses visual recognition and automated image tagging to allow customers to search by image or even find out where to buy products within videos.

Fusemachines bakes personal assistants into Slack to perform many of their mundane and repetitive tasks such as finding leads, following up on leads, cleaning data, setting reminders, creating proposals and contracts, scheduling meetings and drafting emails.

Megastores are using software and hardware to build autonomous shipping centers.

InsideSales automates sales forecasting

SightCorp uses visual recognition and sentiment analysis to see when taxi riders are bored and giving them money back for every minute they were bored.

IKO automates lead generation according to your ideal client specs and automating the end-to-end sales process, from cold calls to ranking leads for humans to focus on.

Enhancing the resolution of noisy images.

NSynth allows you to mix any four sounds into a new sound.

Editing existing images and videos to look realistically different.

AI Capability 2: Understanding text and answering questions in natural language

There are many moments where AI can bring relevant recommendations, predictions, insights or support to users when then need it in a conversational way that doesn’t distract them from what they’re doing.

Bradesco, the second largest bank in Brazil, is applying Watson to enhance support and meet the evolving expectations of its more than 65 million customers. Bradesco first deployed the Watson-enabled Bradesco Inteligencia Artificial (BIA) virtual agent in 2016, with employees using it to answer customer questions about products and services answer customer questions about products and services.

Not a real thing, but you get the point. And it easily could be built.

ING in the Netherlands has made their online banking system entirely hands-free through voice recognition and control.

Mandy AI is a tool designed to help teams improve engagement, understanding and trust.

AI Capability 3: Analyzing data for patterns and anomalies, then deriving insights and recommendations

If your product helps users to manage an organization, a system, or a team, then it’s likely that AI can help streamline the analysis process — even extend it into the future.

Watson helps security analysts at Sogeti work more efficiently so they can react in the most effective way possible. With Watson, Sogeti’s analysts can identify false positives with 23% efficiency.

Adding Watson to existing applications and workflows can boost efficiency.

DataMinr provides real time global data to help companies make informed decisions and automate actions fast.

Personal loans provider Avant pre-vets, prioritizes and completes data science projects faster by using ML to measure the outcome’s impact then prioritize and plan the team’s schedule.

ML can examine images often more accurately than humans.

Cortical.io converts words into semantic fingerprints for fast semantic search, semantic classification and semantic filtering.

AI Capability 4: Understanding needs and recommending solutions

If your product analyzes any kind of data, then it’s in a position to bring insights, predictions and recommendations to your users. You can take it one step further by providing support when they appear to be struggling, have a question, or need to be guided through learning something new.

Accenture uses IBM Watson Studio to bring together data management, real-time analytics, AI and machine learning into one environment, helping their clients address and prevent fraud moving forward.

Versive uses uses standard data sources from across the business to fully recognize connected systems and learn “normal” behavior for your unique environment and visualize the progress of a threat in context.

Ayasdi’s high-performance computers and algorithms can examine big and complex data far faster and seek insights more comprehensively than any human is capable of.

Watson Video Enrichment allows you to see deeper into your video content than ever before, unearthing opportunities to improve content discovery, viewer engagement, ad revenue and more.

Tableau is ranked best business analytics tool for visualizing data to uncover better insights by Gartner.

Zurich Insurance added predictive analytics so they could provide more accurate sales planning and sell more effective policies.

Antuit connects data from across all department to maximize the company’s returns.

IBM Cognos, similar to Tableau, provides a toolset for reporting, analytics, score carding, and monitoring of events and metrics.

The Royal Bank of Scotland has dramatically increased its visibility of customer activity to improve customer service and satisfaction.

OpenTable prevents credit card fraud between their customers and the dining experiences they prepay for through their app.

Descartes Labs analyzes data from a global network of sensors to help companies make real time decisions based on visual feedback of the globe.

OMNI uses ML to improve manufacturing and shipping processes.

AI Capability 5: Personalization

If your product collects data about users behaviors, settings, or system, then AI can help improve their experiences more and more over time.

Nest thermostat learns from your actions and automates your temperature schedule.

Teachers are always looking for quality content that can help them teach their students. Teacher Advisor with Watson focuses the innovative power of IBM Watson on perhaps the most critical — yet all-too-overlooked — aspect of education: helping teachers improve their skills and educate children more effectively.

IBM Watson Candidate Assistant is a cognitive talent management solution that engages candidates in personalized career discussions, beyond their qualifications, and recommends positions that that fit them best. Resulting in better hiring decisions and, ultimately, more engaged employees.

Netflix personalizes artwork based on your viewing history, location, etc.

Many companies are working on using ML to provide customized lesson plans and teaching methods to account for each student’s unique needs and talents.

Amazon’s been using ML for over a decade to make personal recommendations based on your actions.

Lowe’s LoweBot provides both personal assistance as well as shelf audits and analytics reports.

Dynamic Yield uses ML to dynamically personalize and reformat designs based on the user’s context and marketing trends.

AI Capability 6: Providing explainability and allowing users to directly or indirectly improve future responses

If your product provides recommendations, insights, or predictions, it should always allow for progressive disclosure options into how it came to those conclusions. Also, don’t forget that your users are also subject matter experts. Give them every opportunity to tell you how to improve the experience, or even send their feedback directly to the model so it can retrain itself.

Trust and transparency features for AI being made available in IBM Cloud

Watson Health uses vast sources of public and private data to help medical professionals keep track of new discoveries and make the best possible diagnosis.

Thanks to https://appliedai.com for their use case reference library which I drew on extensively for this article.

Jennifer Sukis is a Design Principal for AI and Machine Learning at IBM, based in Austin, TX. The above article is personal and does not necessarily represent IBM’s positions, strategies or opinions.