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How to Fit Artificial Intelligence into Manufacturing, Part 2

As customers demand more customization, less time between new generations of products, and more, design engineers think AI might hold the key to bridging the gap between low or custom, and high or mass-production.

The program can generate 5,000 miles of vehicle test data per hour, when it currently takes over 20 days from physical efforts.

As industries move faster it seem a question of whether faster production will mitigate the cost of waste, or if slowing production to reduce waste is better for production and revenue.

As plants become more automated, AI predictive maintenance that can update models in real-time based on theoretical and real-world data will continue to reduce unplanned downtime to stabilize production time and reduce cost.

As StrongArm Technologies CEO Sean Petterson puts it, “Fall in love with the problem not the solution.” It can be easy to focus on solutions or become distracted with new equipment and miss details or the root causes of the problem.

Like some AI generative design CAD software, MIT CSAIL developed a platform that takes input, such as payload, cost, and battery usage, and produces a custom designed drone.

(Credit: Jason Dorfman/MIT CSAIL) With a big-picture approach and understanding that support must come from the top down, but often change must be pushed from the bottom up, multiple disciplines from a range of experienced senior staff are necessary for new projects to succeed.

Since AI needs a lot of data to work effectively, you will need a lot of brain power and experience to know common and special cases, anomalies, and where different tools could provide the greatest benefit with a focus on root cause analysis.

   According to an article from McKinsey, if an AI solution is to be developed in-house,, it will need AI creation experts that have technical, change-management, and business skills in addition to multiskilled project managerwho can act as translators. It is necessary to integrate process engineering, data science, business strategies, and management expertise into an AI solution.

Working with a people outside of an employee’s day-to-day contact or normal disciplines will help offer insight to see where operations could improve, what valuable features could be added, or what additional revenue streams exist.

   Where AI differs, according to McKinsey is, “Building a strong in-house team of IT and data specialists is a priority, as their education focuses on the key elements of AI: computer science, databases, data architecture, modeling, statistics, analytics, and mathematics.

Within this group, you might consider data engineers, who are able to manage and navigate data-storage solutions and protocols, and data scientists, who can interpret and process data and create algorithms and models to solve complex, multivariable problems.” As AI in manufacturing progresses, finding good, experienced data scientists might be difficult.

Once the problem is understood, you’ll shift your focus from what’s out there and what it does to the key points of my problem, the specs that a solution must operate within, and the clear objectives and results trying to be obtained.

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If there’s one topic that makes a radiologist’s eyes widen, it’s any mention of deep learning (DL) and how it will impact their careers.

Whether it’s a fear of being replaced by robots or a genuine interest in the technology, AI and deep learning are hot topics for medical professionals.

While AI was first created in 1956 by computer engineers at Dartmouth College, it was meant to address problems that were difficult for humans to solve but simple for computers.

Scientists knew it was possible for machines to understand and make sense of large volumes of data, they just weren’t sure how to make it happen.

Teaching machines algorithms to parse data, learn from, and make a determination saved scientists time and effort because they no longer had to specify each data point to the computer.

Scientists began testing convolutional neural networks (CNNs), a class of deep, artificial networks that are similar to biological nervous systems.

Unlike machine learning, these networks do not form a cycle, enabling them to make new determinations based on massive quantities of data.

(Source) The fascination spiked in 2012 at the ImageNet Large Scale Visual Recognition Challenge when scientists were able to prove that out of the top 5 labels chosen by deep learning algorithms, the chance that an incorrect label will be included was reduced by 10%.

Getting the most accurate image possible required the medical professionals to intervene in each step of the process, even with advancements in computer film colorization in the 1990s.

When deep learning models are trained with images labeled by experienced radiologists, it has the potential to be a training tool for future or early career professionals.

These particular medical fields lend themselves to deep learning because they typically only require a single image, as opposed to thousands commonly used in advanced diagnostic imaging.

AI in Banking – An Analysis of America’s 7 Top Banks

While tech giants tend to hog the limelight on the cutting-edge of technology, AI in banking and other facets of the financial sector is showing signs of interest and adoption even among the banking incumbents.

In this article we set out to study the AI applications and innovations at the top banks, helping you to answer the following questions: Through quotes from company executives and data from our AI in Banking Vendor Scorecard and Capability report (interested readers can download the Executive Summary Brief), this article serves to present a concise look at the implementation of AI at seven of America’s top commercial banks by revenue.

First, we’ll provide some insights about the state of AI in banking we discovered through exploring our data and interviews with banking executives Readers with a broader interest in AI’s applications across the financial sector may be interested in reading our article Artificial Intelligence in Finance, which covers a wider array of applications beyond the top US banks.

The full breakdown of AI vendor product offerings by function is provided in the graph below: We should note that banks are likely understating their use of AI for other use-cases, and the banking experts we interviewed for our report and our AI in Banking podcast all agree that banks are investing in AI for compliance and risk monitoring more than any other business area.

At the same time, banks are likely overstating their use of AI for customer service applications, including chatbots because: It will be a while before the technology has advanced enough for chatbots to generate natural language and hold conversations with customers more often than they’re routing customers to customer support agents.

Lastly, our research found a number of top banks referring to AI as an “augmenting” force for their employees, not a “replacement.” To us, this seems to be a necessary move of the communications department, but a disingenuous way to describe AI’s potential impact on jobs, which will most likely involve both “augmenting” and replacing human beings outright.

Unlike many modern tech giants, old banks often have thousands of employees performing mundane paperwork and “legacy” processes, many of which may require complete elimination once machines can replace humans at the desk.

The Emerging Opportunities Engine, introduced in 2015 and discussed in a letter to shareholders, purportedly uses machine learning and natural language processing to help “identify clients best positioned for follow-on equity offerings.” The technology has proven successful in Equity Capital Markets and the company stated their intentions to expand it to other areas, including Debt Capital Markets, but it’s unclear if this has happened yet.

Wells Fargo hasn’t publicized many artificial intelligence initiatives, but Steve Ellis, head of the bank’s Innovation Group, seemed eager to leverage AI in a 2017 press release for a chatbot pilot: AI technology allows us to take an experience that would have required our customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment.

Katherine McGee, Head of Digital Product Management at Wells Fargo, elaborated on one of these prompts in an email to Bank Innovation: If a customer receives an incoming deposit which is not in their usual pattern of transactions and is not needed to meet their normal expenses or scheduled payments, we can highlight the deposit and suggest the customer save the funds.

Consistent with our high-tech, high-touch strategy, we’ll continue evolving our best-in-class digital banking capabilities, including Erica, to provide clients relevant, timely guidance and help make managing their finances easier.

Although the demo video below shows how Feedzai’s software works for eCommerce companies (and is admittedly a little corny), the principles it describes can certainly apply to banks: Feedzai’s software will purportedly monitor customer payment behavior for deviations from that customer’s normal payment activity.

The software is explained further in the video below and a promotional video can be found on US Bank’s website: According to US Bank, using Expense Wizard, a hiring manager can provide a virtual card to a candidate via the app, setting a card limit via US Bank.

PNC invested $1.2.billion over five years, according to its 2016 annual report, into modernizing its “core infrastructure and build[ing] out key technological and operational capabilities,” with the objective of faster, more secure and more stable operations and services.

Still in the early stages of its tech strategy, the company’s initial focus has been on the consolidation of its data centers and a major shift to an “internal cloud environment.” We can presume that the company’s infrastructure upgrades will help them leverage data and implement artificial intelligence and machine learning.

Examples include “data requests from external auditors” and “funds transfer bots” which help “correct formatting and data mistakes in requests for dollar funds transfers.” The video below provides an explanation for how Blue Prism’s AI software works: Former Senior Executive Vice President and Global Head of Client Service Delivery at BNY Mellon Corp, Doug Shulman, said this about the bank’s investment in RPA: If you think about smart automation, robotics is a piece, workflow is a piece, and we’re combining smart forms, optical character recognition, workflow and robotics to get momentum around automating tasks.