Good News, Humans: AI Still Needs Us (for Now)

May 23, 2018 · 5 min read
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Companies developing AI and machine learning systems need to acknowledge that they’re not infallible and remain ‘teachable’ via human intervention.

By S.C. Stuart

I had a run-in with the power and limitations of AI last week when I ordered a Lyft to take me home. A new driver pulled up to the curb but forgot to tell the Lyft “brain” that I was in the car. We drove off, and the driver’s system soon buzzed with an alert.

“You’re not here,” said the driver, confused. “I’ve been assigned a new passenger.”

As I always order an ecologically-friendly “Lyft Line” with up to two other passengers, I wasn’t too bothered. Until I looked down at my phone, which said I wasn’t actually in the car. The driver was apologetic, but said there was nothing he could do. “I have to follow the route mapped out for this new passenger.”

Infuriated, I got out of the Lyft, as another bemused passenger got in.

Here’s where it got interesting (IMHO). I instantly contested the $5 fine from Lyft for “not being ready for the Lyft Line” and ordered another driver. Then I could almost “see” how the Lyft system went through its AI “thought processes” for risk assessment on handling my case.

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First, it would look at my history as a rider (excellent: always on time, no credit issues on payment). Then (I assume), it ascertained my “score” by seeing how many rides I’d taken (frequency) coupled with revenue gained. This would give it a baseline “model” (my participation in the Lyft service) and unique risk assessment “score” to handle any issues on my account.

The complaint process was handled by the AI pretty smoothly — until I disputed the credit and selected the option to have a human take over. It all ended well. They had access to the same “score” as the AI so there was no delay as the representative went through my details. But that’s because Lyft built a “human-in-the-loop” into its AI-powered system.

The lesson, for me (and hopefully for you too) is that companies developing systems that run on AI and machine learning need to acknowledge that they’re not infallible and remain “teachable” via human intervention.

Algorithms Make Life Decisions

I’ve been to several “better living through algorithms” symposiums recently, but few get beyond “bias is bad” and “something must be done.” Simply put, we need the ability to train AI, but how?

I put in a call to Dr. Jason Mars, a computer science professor at the University of Michigan. He’s currently on leave as director of the university’s Clarity Lab and is the co-founder and CEO of Clinc, a conversational AI startup for the financial industry.

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“One of the greatest challenges in this age of AI is enabling the masses to wield and train the types of machine learning models that only the top computer science experts of the world have been using,” said Dr. Mars. “At Clinc, we invented a new class of training platform to address this exact problem.”

Clinc’s platform, known as Spotlight, “can train and retrain the best AI models on the planet without having a computer science or AI background,” Dr. Mars said.

Essentially, Clinc built a front-end tool disguised as a conversational AI bot. Through natural language processing, it can allow customers to investigate and change what is known about their financial patterns.

“This is a hard science problem,” but advancements in the space mean “users can create new capabilities in managing and observing their financial accounts and spending patterns,” he said.

Watching an AI Think

“AI models are subject to concept drift,” Christian explained. “Which means the model needs to be retrained to take into account new data that has ‘drifted’ away from what initially trained the model. Every time AI models — including TEVI’s — are retrained, you could argue that the users have re-calibrated the model.”

However, as he pointed out: “Peeking into the AI blackbox to see its rationale is a more difficult proposition. Cutting-edge research is experimenting with masking certain layers of the neural network in order to isolate variables and understand how the model is perceiving certain features. But it may be awhile before we fully understand what’s happening behind the curtain.”

Changing Machine-Learning Methods

“Building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction,” she said. “It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning.”

In her opinion, the computer science research community must rethink software development tools such as debugging, testing, and verification tools for complex AI- and ML-based systems.

According to Dr. Rana el Kaliouby, founder and CEO of Affectiva, building effective, quality AI begins and ends with carefully designed data collection.

“You start by digging into the specific use cases of the AI you’re designing, and then focus on collecting large amounts of real-world data that is representative of these use cases. This is crucial in order to ensure that algorithms perform accurately in the real world,” she said.

“For example, when building a driver drowsiness detector, you need a lot of examples of people getting drowsy behind the wheel. We do not think it is ethical to sleep deprive people and send them down the highway. Instead, we collect large amounts of driving data ‘in the wild’ so we can mine for natural occurrences of drowsiness. Once the AI is deployed, it is important that data comes back to R&D in a continuous feedback loop, so that you can validate and, if necessary, retrain your models.”

Read more: “This Company Wants Your Gadgets to Understand Emotions

Originally published at

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