Where is AI today, and what is real vs what is hype?

 

During the last 10 years, there have been many impressive advancements in AI, with the last six months bringing on a rapid acceleration in AI technologies.

On the other hand there are significant issues around AI that need to be addressed – issues of bias and fairness, reliability and responsibility of AI. How do we monitor these systems, govern them?

 

How do we ensure that they are trustworthy?

Underpinning these questions are specifics that we hope to tackle in this series, and in this first episode:

 

• • AI investments are in the hands of a few, and we need more democratization of AI both from a usage standpoint as well as an investment standpoint.
  • AI expertise is extremely limited worldwide. The people who understand these systems – how they work, why they are behaving as they are, how to monitor and govern them – is a very small group. AI education is critical.
  • AI needs to understand the real world, the human experience, in order to have impact.
  • AI is incredibly expensive to run, and we need to bring down the cost so that it is more accessible for all, and responsible not just from an accuracy standpoint but from an environmental-impact standpoint.

These are big issues that aren’t easy to solve. We tried to take a thoughtful approach to appreciating the breakthroughs while examining the limitations so that we can all benefit from the ways AI can enhance humanity. The ways AI can be human-centered, in order to bring real value.

We announced our video series, “A Conversation about Human-Centered AI” last month and are excited to share Episode One, Part One: AI Hype vs. Reality.

Let’s start a conversation! Tell us what you think about AI hype vs. reality on LinkedIn or Twitter. Or reach out to see how your company can navigate the hype vs the reality for your organization at info@vian.ai

There’s never been a more important time to dive deep into the complexities of AI. In our new video series, “A Conversation about Human-Centered AI” with Vianai’s Founder & CEO, Dr. Vishal Sikka

We explore important questions and challenges facing AI advancements, from ethical considerations and bias issues to government regulations and the education needed to ensure AI is responsible, trusted, and aligned with the advancement of humanity. Tune in as we explore the exciting breakthroughs in AI that we have seen accelerating in recent months, as well as the sometimes-uncomfortable questions around the reality of AI today, and how it should be managed so that it is trustworthy, reliable, and human-centered.

Take a look at the sneak peek here:

Lean Operations

Lean operations have been the holy grail of manufacturing for decades with a variety of tools and processes applied over the years, such as Kanban, Poka Yoke, and Kaizen. Yet, often, the systems rely on many working behind the scenes racing to meet tight customer deadlines, ensuring supplier delivery, and keeping the warehouses as lean as possible. This results in a monthly sprint cajoling suppliers, juggling inventory, and managing clients.

Against this backdrop, one very large manufacturer asked us to help bring advanced AI technologies to predict, as early as possible, when a supplier would miss a deadline.

The Problem

At the root of our customer’s problem was stranded inventory cost. Essentially, because our client built large, complex, and often custom-made products, which involve tens of thousands of components, one part missing or late could delay the rest of the product from being complete. This occurs regardless of the cost of the component, i.e. a $1,000 part could delay a several million dollar product.

Today, for our customer, this results in $150 million per year, nearly half a million dollars a day, in inventory costs.

Our customer needed to have the ability to intercede as early as possible. Today, the system functions on a very human basis. Supply chain managers often know who might need more time, which suppliers are habitually late, and which parts are irreplaceable.

In the end, the ML models reduce the error in classifying late orders by more than 60 percent.

Yet, even the best supply chain managers can’t accurately map out tens of thousands of parts. And, worse yet, often the ERP systems they’re working on don’t have reliable nor accurate information.

So, in short, the problem for our customer came down to several dimensions:

• One part could delay an entire product, regardless of the cost of the part.
• The supply chain professionals couldn’t look across a complete product’s thousands of parts to accurately predict where to put their attention.
• And the systems they use had unreliable datasets.

The Solution

We started with tying our customer’s many databases together. These yielded several thousands of tables of data. From this, we interviewed the main supply chain managers who work on these problems daily.

From their notes, we generated hundreds of features along with additional tables through our analysis, ML deep learning and feature engineering work, which leveraged our extensive understanding of supply chain management processes and ERP systems.

We learned how they interact with the data, what types of signals they look for, and what kind of tools they needed to be more successful. In this effort, we discovered a few key insights:

• Intercession was a key component to a successful tool. Our aim should be to reduce the number of suppliers that a manager had to consider.
• The system we built would need to use the ERP tools in ways that our client hadn’t considered.
• Our system would need to provide tens of thousands, even 100,000 predictions in a day across all the parts in all the product lines.

The true difference and difficulty in our system comes in how we have adapted it to work on incomplete data. Using our customer’s faulty ERP data, we could predict late deliveries at eight weeks out — the most important time to make a prediction about a supplier because it allowed buyers to react to a change in the expected behavior.

Furthermore, as the delivery date approaches system accuracy improves. Eight weeks was viewed as a key point in tension between the absolute certainty in a prediction and providing enough time to take mitigating actions. Our client also indicated that a supplier changing its delivery date eight days before delivery is equivalent to completely missing it, because it’s too late for a change in behavior to react. Our system then classified all last-minute changes as being missed deliveries.

In the end, the ML models reduce the error in classifying late orders by more than 60 percent.

That translates into 60 percent reduction of effort for users (i.e., procurement managers) to contact suppliers. For orders expected to be late, the error in delivery date prediction is reduced by more than 50 percent by ML models as well. That is a great improvement, given most orders have long lead time (1+ year).

The Results

Our customer had a complete model that could accurately predict a late delivery or commit failure with enough time to allow supply chain managers to intercede before it endangered the shipment date of their product and caused stranded inventory issues.

We emplaced a robust ML system using the data they had on hand, deriving value that they had not realized existed. This system allowed for greater trust in the system, and greater transparency, and amplified the abilities of the supply chain managers to triage the most important and challenging problems.

For more information on what we did for this particular customer, and to find out how we could work with your company to solve complex problems using AI and ML capabilities, reach out today.

Let’s meet at the Toronto Machine Learning Summit!

We are looking forward to a great event this week at the Toronto Machine Learning Summit! @Sue Dunnell @Jeremy Jiang @Max Bergstein and Srikar Srinath from the Vianai team will be onsite to join the discussion and explain how we can help data scientists, machine learning engineers, machine learning operations teams and others.

We are ready to help businesses bridge the gaps across the AI lifecycle to monitor models for drift, bias, uncertainty and other risks, monitor models at a very large scale, and dramatically increase model execution speed and throughput. We’ve worked with companies to accelerate model inference speed more than 100x while reducing the model’s footprint by 300x. We are able to run state-of-the-art AI models on limited compute surfaces without taxing firewalls or routers. We also know last-mile issues cause delays when figuring out when to retrain and redeploy models. Our next-generation monitoring capabilities help eliminate alert fatigue, and make it easy for ML engineers – or anyone tasked with monitoring models – to understand why models are drifting, and we suggest actions to take to keep models trustworthy while running in production. We call it our continuous operations process, and it includes wizard-driven policy creation for each model, highly flexible and granular custom thresholds for distance and window-based monitoring, and the ability to view billions of data points in a single window. We provide retraining recommendations and make it easy to use advanced deployment techniques like challenger/champion models. There’s a lot more, and we’d love to tell you all about it.  

Attending? Find us on the Whova app to set up a meeting or visit us at Booth #2.

 

We can’t wait to meet with you!