The Risks And Rewards Of Artificial Intelligence For Lenders

In looking at this, the recent debut of self-driving cars could transform a stressful commute into an opportunity to tackle emails and reading lists while making suburban long-distance travel great again. Americans are poised to gain more than 100 hours per year in free time by relinquishing the wheel to smart cars. The downside, though, lies in inevitable vulnerabilities like security threats, job loss, and environmental impact. Is the reward of AI worth the risk?

Artificial Intelligence is broadly defined as a computer’s ability to perform tasks normally requiring human intelligence. Ever since Alan Turing developed a test to determine a machine’s ability to exhibit intelligent behavior in 1950, roboticists and scientists have sought to pass it. In 2014, a computer program called “Eugene Goostman” succeeded by convincing 33% of the human judges that it, too, was human. Since then, companies like SAP, General Electric, and MasterCard have utilized machine learning and artificial intelligence (MLAI) to identify trends and insights, make predictions, and influence business decisions.

Artificial intelligence offers new opportunities to revolutionize operations in the financial services industry. Machine learning can process terabytes of data in seconds – volume which a horde of humans with older machines or methods could not process in a lifetime. The sheer power of modern computing in assessing an increasing number of variables with speed and accuracy gives a financial institution the ability to have rapid and strategic insights about customer behavior, reporting errors, and risk patterns. In the area of loan decisioning, this should lead to faster loan origination, fewer compliance problems with regulatory fines, and more inclusive lending overall.

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We wanted to know: What are the risks and rewards of AI in lending, and is it an inevitable next step for compliance management?

Bob Birmingham, CCO of ZestFinance, and Dr. Anurag Agarwal, President of RiskExec at Asurity Technologies, explore.

Which Discrimination Would You Prefer: Human Or Machine?

Anurag Agarwal: With machine learning, the decision-making is supposedly agnostic to overt biases. Humans, by definition, are free thinking but with biases that interfere with decision-making, especially in lending scenarios.

Bob Birmingham: AI is not a product but a solution, another approach to problem solving using advanced mathematics, analytics, and data. It’s not a magic fix but can remove some of the human discretion that leads to discrimination.

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AA: The problem is that computers could reinforce societal disadvantages by relying too heavily on data patterns. When a machine imports data, it creates an algorithm but cannot explain its methodology. It may introduce variations in the mix without understanding the broader implications.

Say you are trying to determine the risk of repayment for a borrower. The machine identifies physical appearance as a decision point. Based on data patterns, it detects that blue eyed individuals are more prone to timely repayment of loans. Noticing a correlation, it elevates that decision point to a higher influence in future lending. By taking an apparently agnostic data element and making a correlation it has in essence created discrimination because it doesn’t understand that blue eyes are mostly representative of Caucasians and the societal implications of making that borrower preference as a result.

BB: We’ve been here before as an industry. When regression modeling first came out it was confusing, flawed, and its accuracy questioned. At the end of the day, it was actually a more accurate process than purely judgmental underwriting because it followed a clear set of instructions to find answers. MLAI creates an opportunity for continued improvement to mitigate and eliminate discriminatory lending practices.

Risk: Machines can learn the wrong lessons from data.

Reward: Machines don’t have overt biases.

Alternative Data

AA: AI is very new. We don’t yet know how to regulate it or what its long-term impact is. As soon as data began generating, we followed this “go forth and multiply” pattern. Now we have more data than we know what to do with. Also, a lot of this data is unstructured.

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BB: Alternative modeling and alternative data are often lumped together, but that shouldn’t always be the case. MLAI alternative modeling may be used on the same data that current regression models run on and provide lift. Alternative data may be used with your existing models too but is more often used in conjunction with alternative modeling due to MLAI’s ability to handle large data sets. MLAI also has the ability to identify missing, erroneous, or wrong data and solve problems related to the inaccuracies.

AA: Alternative data, such as Facebook profiles, Twitter feeds, and LinkedIn pages, are already used in employment. HR departments use this publicly available information to determine if you are a qualified candidate. By knowing this in advance, you can change your online behavior to skew these data points in your favor. That’s the big question with alternative data in the lending space, along with privacy and transparency issues. We don’t know who gets to manipulate the data, where it comes from, or who has access to it. There’s no established system or standardized data points, which means most companies must follow their own proprietary lending rules, making it a regulatory free-for-all.

BB: While there are caveats, the rewards significantly outweigh the risks. There are many underserved individuals, businesses, and micro-borrowers with little or no credit that could benefit from alternative data. If an applicant doesn’t have good credit, or any credit at all, lenders can use MLAI techniques to paint a richer portrait about the borrower’s reliability using nontraditional factors like e-commerce histories, phone bills, and purchasing records. MLAI can open up the credit market, measure patterns, and fill in the data gaps, giving lenders a more holistic view of an applicant. Alternative data doesn’t have to be “creepy” data and doesn’t have to be social media data. Responsible and transparent use of alternative data to expand access should be encouraged.

Risk: The use of alternative data in credit underwriting raises privacy, transparency, and data integrity issues.

Reward: Alternative data can increase financial inclusion by granting access to capital to individuals and businesses with no traditional credit history.

Hackers vs. Trackers

AA: As we saw with Equifax, any time data is automated through the cloud there is the risk of data hacking. Now we are asking: who is responsible for protecting all of this data? Using such a detailed lending profile increases the necessity for data privacy and security.

BB: These are risks but the industry has always been responsible for protecting sensitive data.  The good news is, the more information we have about a borrower, the quicker we can identify errors and anomalous behavior. This is a great consumer benefit. Using the same Equifax example, imagine if we could say “this person was affected, and these actions are very different from their previous activity. This is a red flag that their information was stolen.”

Banks raise red flags when uncharacteristically large payments are made or a card is used in a different country, but imagine how much more effective these alert systems could be with additional insights into an individual’s unique behavior patterns.

Risk: More data in the cloud means a higher risk to consumers in the event of a data hack.

Reward: Additional data can identify unusual behavior quicker, which is a benefit to consumers.

The Regulator’s Dilemma

AA: Regulators have a big job ahead of them figuring out how to regulate companies using this information. Many of these companies aren’t sure how to use it themselves.

BB: Initially, users of MLAI in high stakes applications have an obligation to educate the public, create a set of best practices for their industries, and be transparent. Financial inclusion is something regulators support and this technology can help lower the barriers to entry.

AA: In late 2017,the CFPB issued a no-action letter to Upstart Network, an online lending platform that uses both traditional and alternative data to evaluate consumer loan applications. The terms of the letter required Upstart to share data with the CFPB about its decision-making processes, consumer risk mitigation, and methods for expanding access to credit for traditionally underserved populations.

By studying these companies, regulators can better understand the impact on credit in general, on traditionally underserved populations, and on the application of compliance management systems.

Risk: Regulators are still learning about AI and how to properly monitor it.

Rewards: Regulators support consumers and want to make access to credit more inclusive.

The elephant in the room: Jobs.

BB: Typically, Financial Investigative Units (FIUs) are looking at alerts 24/7, researching a person, tracking where money is going, and determining if they should file a suspicious activity report. It’s the banks biggest compliance cost and their highest area of employee attrition with numbers ranging from 15 – 35% turnover in the FIUs. With AI, FIUs can focus on stopping financial crimes rather than toggling back and forth between 15 screens and parcelling through tons of information. This should free compliance personnel up to do higher value, more rewarding work in addition to driving more efficient outcomes for their organizations.

AA: I prefer a human loan officer over an automated machine-learning system. If something happened six months ago that caused your credit to go down, you can explain that to a loan officer. How can you convey that to an automated system? Those intangibles make human interaction necessary. I believe there is something valuable in interpersonal interactions that can never be captured in a truly automated fashion.

Risk: Headcounts in certain departments that are reliant on manual processes could decrease.

Reward: New and more rewarding job responsibilities will result in less turnover, but ultimately there is no replacement for interpersonal interaction for certain positions.

Closing Arguments

BB: Currently, the industry operates with a “look back” approach for Fair Lending, which doesn’t really work. The whole process of defending and explaining discrimination after a model is put into production feels outdated. Today, we can and should build AI models to proactively remove discrimination before ever putting a model into production.

AA: This technology is still very new. Medium to big size lenders should let the technology emerge and wait to see what the regulatory landscape looks like. Regulators still have a ways to go before any of us will fully understand what the future looks like for AI in the lending space.

Artificial Intelligence: The Pros And Cons

Here’s hoping you got exactly what you wanted for Christmas! You did want an Artificial Intelligent loan application, didn’t you? After all, based on what we saw at the convention in Denver and all the publicity since then, this is what most mortgage bankers had at the top of their list to Santa.

Without a doubt, the full force of this change is sweeping the industry. Whether it is called AI, Machine Learning, Digital Mortgages or has a unique name, lenders are looking for ways to add some means of streamlining the application process. However, not all of this is necessarily good news. In talking to numerous loan officers and technologists in the industry it appears we are headed for a consumer and secondary crisis of confusion. This potential confusion crisis has many different causes.

Industry members from all segments are asking if every automated option is really “artificial intelligence” and what it means to them. Some are concerned that the expense of developing such a system is far beyond their means. Overall however, they just are not sure what it is and what to do about it.

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The quick answer to that question is obviously dependent on what you want a machine to do. Today the term artificial intelligence is discussed by many in our industry as a single effort which will take in information and use it to make an underwriting decision. In other words, an “expert system”. What’s so hard about collecting data and sending it to an automated underwriting system? We already do that. But is that really AI?

In its most simplistic form “AI” is a machine with human cognition, but there is much more to it than that. Just as individuals are not born with the knowledge they have today, a machine must learn. This means learning facts and, while humans are capable of innately associating the facts with the problems, it must learn how to associate these facts appropriately with the problems it is trying to solve. How much we teach machines is dependent on what we want it to do as well as how much information we give it. Machines with the ability to think like humans not only have millions of facts but can use these facts in a “Neural Network”. In other words, they can reason based on what they know.

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Lesser types of AI include programs such as Expert Systems, Machine Learning or RPAs. While these programs are all based on the core expectation, their abilities are very different. For example, Expert Systems are a method of automated reasoning based on a very specific set of facts, rules and principles. Applying a user interface that asks a question, they will filter the data with the rules they have. If the rule is not there they cannot “reason” an answer.

Machine Learning development involves the construction of algorithms, which can learn and make predictions or decisions based on the data incorporated into the program. This type of AI is most often used in such areas as data mining since it uses statistical analysis to identify patterns. Using the data available it makes predictions about any new data it receives. Machine Learning algorithms include such statistical methods as regression analysis and decision trees. The education of this type of program is conducted by running large amounts of data through the model until it finds sufficient patterns to make an accurate decision.

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RPA or Robotic Process Automation mimics user activities and can process structured and semi-structured data in line with the rules embedded in the program. It is highly deterministic. In other words, based on the information received the output will be what the rules have defined. These programs are frequently being used expert systems and typically include a “human-assist” factor that deals with exceptions that do not meet the rules in place. However, one of the values of using RPA technology is that this program can “learn” or add rules based on the user actions. For example, if such a program is evaluating the completeness of a document, the rule may say that all fields must include data. In this instance, a document with an empty field would become an exception. If, however, when this occurs in a specific field it is labeled an exception and the human interface says it is “OK”, the machine will adapt the rules to allow a document with this empty field to be labeled acceptable.   This type of AI is used primarily with OCR technology and employed in operations, which currently employ a “stare and compare” process.

In developing any of these systems, it should be evident by now that the common and prevalent factor in any of these programs is the data. Types of data received by any organization include structured data, semi-structured data and unstructured data. A true AI program can utilize all three types of data whereas the lesser options are limited to structured data, and in some cases semi-structured data. Unstructured and semi-structured data is sometimes referred to as “dark data”.

Here is where the issues and risks begin. Everyone is familiar with the term GIGO or “garbage in; garbage out”. Without consistent and accurate data any “thinking” done by these programs is suspect. For years, the industry has struggled with developing a single source of data with a consistent definition and characters and still has failed to ensure that the data used is accurate. This however is not the only issue when it comes to data utilized in an AI effort. There are also a variety of other data issues. Among these are “noisy data”, “dirty data”, “sparse data” and “inadequate data”. These issues result in having conflicting or misleading data, missing or erroneous values, and incomplete data. While MISMO has done a tremendous job of structuring data definitions, programs being developed must have some type of pre-analytics that can be applied to raw data to ensure standardization, imputation and other basic techniques to ensure the quality of the data.

A second issue with data is its security and governance. Each entity must develop a method for ensuring that the data is managed properly. In other words, establish acceptable sources of data, determine how it will be analyzed for consistency and accuracy and identify who has control of the final data set so that it cannot be accessed by those without authorization. Data governance involves the management of this resource in a way that ensures its quality and use while maintaining the ability of the organization to capitalize on the opportunities presented.

In addition to data there are other critical issues that must be addressed by any company seeking to utilize AI technology. Chief among these are talent and culture. One of the primary issues faced is the talent pool necessary to sufficiently manage, develop and execute analytic projects involving machine learning. Data scientists, those individuals skilled in computer science, math and domain expertise are necessary to develop and maintain these AI programs. However, there is a shortage of data analytic talent in this industry. On the down side, many employees will quickly lose relevance in the workplace as much of the tasks they perform are taken over by machine learning technology.

Another potential employee change-over will be found in the level of consumer interaction. With the advancements of expert systems and virtual assistants, consumers can ask questions, receive answers and proceed with their applications without a human interface. While the feedback so far is mixed on who is most likely to use such an approach, Loan Depot has already engaged a company to develop an interactive consumer facing system to assist potential customers in this process. Another company, Neutrino Financial Services is developing a program that will allow consumers to obtain answers to questions and start the application process. When ready they can then select a company from a broad listing of lenders, for whom they want to apply. All this without the use of a loan officer. The applications taken in this program will then be transferred to the selected lender, thereby giving the consumers more choice and the lender more access to a variety of applicants.

If the use of AI, in all its various forms, is to be successfully implemented in this industry, a cultural change also must occur. The long-lasting impact of any AI implementation can only be made when a fundamental shift in an organization’s culture takes place. Although there has been much discussion about data driven organization, little of the effort to make this happen has occurred. Job types and job descriptions must change along with business processes and technological solutions are necessary. In addition to investing in the technology, businesses must invest in the appropriate training of staff and process redesigns if AI is to be successful.

However, the application process is certainly not the only area where AI will have an impact. We are already dealing with AUS systems and the accompanying deletion of underwriting staff. This however is just the beginning as much of the clerical functions can use Machine Learning AI with a small contingent of personnel available to deal with the exceptions. Then of course there is the closing process. Today we have acceptance of digital signatures and electronic delivery of the closing documents. While there are some states that will not yet accept digital signatures, this too is rapidly changing. Of course, the biggest issue at closing is yet to be tackled; that of explaining what all these documents mean and what they require of the consumer. However, with a program such as the one being developed by Neutrino, consumers will have access to the resources necessary to address these questions by phone, tablet or laptop.

One of the most obvious uses of AI is in the servicing environment where most of the administrative work is primarily clerical. The potential for the use of AI, including expert systems and machine learning is tremendous. The processes that take place here are prime targets for this type of change and would reduce the overall costs of servicing significantly. Default management could also benefit from these programs as “Bots” are developed to predict potential delinquents much sooner through statistical analysis of payment history and changes in credit status. Assistance with notifications to borrowers would also establish a more beneficial arrangement of calls and relieve some of the staff when applicable.   Another usage would be in the identification of property value changes.

Utilizing the data collected and standardized will assist in congregating loans to form MBS pools. Because of the data quality available from a data-driven organization’s focus, the secondary market will have confidence in the information provided. In addition, utilizing the existing risk model to establish operational variance risk, these institutions will be able to effectively price loans. By having loans accurately price for the performance risk will make them a more valuable investment tool and enhance the overall buying and selling or these assets.

There is no doubt that AI is coming to the mortgage industry and there is also no doubt that in order to reap its benefits, the industry must change. Only when we recognize that artificial intelligence brings with it, costs and risks that will redesign the familiar, and accept what is to come, will this intellectual achievement be achievable.

About The Author

The Good, The Bad And The Reality Of AI


When I was getting my Masters in Business Administration, one of my professors lectured about the future of business. One example he used was from a futurist’s ideas on the factory of the future. According to this individual the factory of the future would have two employees: a man and a dog. The man would be there to feed the dog and the dog would be there to make sure the man did not touch anything.

While this tale may seem laughingly far-fetched, conversations held at the recent technology conference seemed to indicate that this scenario is within the realm of possibility. Numerous discussions were held about the use of artificial intelligence (“AI”) within the mortgage banking industry and ranged from rules-based programs to utilizing programs such as optical character recognition (“OCR”) and validating document collection to produce the first “bots” within the industry. But is this really AI or is real artificial intelligence the actual use of computers that can emulate human thinking processes and contain human drives such as hunger, power and self-preservation?

Controversy abounds on the subject and not just with potential users but among the most advanced thinkers in this area. Their thoughts and beliefs are wide-ranging from the concept that AI will likely play a part in human extinction to those that believe it will improve people’s lives and give them more family and leisure time. An article in April’s Vanity Fair magazine quotes Elon Musk, the developer of Tesla cars and cost-efficient rockets allowing for the settlement of Mars, as believing AI is humanity’s “biggest existential threat.” On the opposite end of the scale, Ray Kurzweil, a futurist, has predicted that we are only 28 years away from the point where AI will far exceed human intelligence and humans will merge with this super intelligent program to create the hybrid beings of the future.

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While arguments continue at this theoretical level, most of these individuals agree that one of the greatest drawbacks to the full use of artificial intelligence is the interaction currently necessary between humans and these tools. In other words, for these programs to work, a human must verbally ask a question, make a statement or key in information. This problem goes away however with the merger of biological intelligence (human thought) and machine intelligence. To accomplish this, companies are currently working on an injectable mesh, called a neural lace, into the brain that can flash data from your brain wordlessly to your digital device or to the cloud, thereby creating unlimited computing power.

With the on-going merger of AI into businesses there is also concern about how it will be managed and controlled. Current public policy on AI is largely undetermined and the software is largely unregulated. Some of the biggest technology companies have taken it upon themselves to develop a partnership on the subject in order to explore the full range of issues, including ethical concerns. The European Union is also deeply concerned and is considering such legal issues as whether robots have personhood or should be considered more like slaves as found in Roman law.

But the question overriding all of these issues appears to be what exactly is artificial intelligence? Is it simply a bot-like program that runs rules that do simple labor intensive work or is it actually the ability of machines to think as humans and take over the entire workload of any business. And if so, what does this actually mean to employees in those businesses? Will their jobs be replaced with robots that not only collect information and compile data for a rules-based engine but actually make decisions based on the data fed into the machine’s intelligence.

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Most importantly to mortgage lenders, what does all this mean to our industry? While the fun and intellectual stimulation that comes from brain-storming these ideas generates lots of enthusiasm, if these concepts become reality, we need to be prepared to utilize them to our advantage and not be thwarted by extensive costs and back room operations.

One way to envision how these AI programs will impact the industry is to look at what has been happening in similar operations. An article by Penny Crossman in the March 16, 2017 American Banker entitled “All the Ways AI will slash Wall Street jobs” gives some insights into what we might expect. According to the author, Opimas, a capital markets consulting firm, projects that by the year 2025 artificial intelligent technologies will reduce employees in the capital markets profession by 230,000 people. Furthermore, spending for AI-related technologies is expected to be more $1.5 billion and will reach $2.8 billion annually by 2021, just four years from now. This number does not even include the start-ups that capital market firms will invest in during this period. All of this expense is expected to be offset by a 28% improvement in their cost-to-income ratios.

So where are these programs being placed? The first functions being replaced by AI technology are process-oriented jobs. These jobs are actually being replaced by lower level AI functions that are programmed to do such things as look up documents, find data and compare multiple data sets.   In addition to these process oriented jobs, those whose function is to conduct analytics on the data are also being replaced with such technology as machine learning functions. In this “deep learning” technology, AI programs digest large volumes of real-time data within a very short period of time and then “learn” to find patterns that provide insight and direction at a speed humans can’t begin to match.

Another area of capital markets feeling the impact of AI is front office sales personnel. Since initiating AI technology in this area there has been a 20% to 30% drop in headcount. In addition, many jobs in the middle and back offices are also feeling the impact. Since the majority of these jobs are processes that are connected by human manual intervention AI that brings with it image recognition can replace this human activity.

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Compliance concerns that resulted in significant headcount increases are now being taken over by AI programs that validate specific documentation and provide a more holistic view of the regulatory risk and organization compliance trends. This is one area where IBM’s Watson is proving extremely valuable.

The implementation of AI in capital markets gives an excellent overview of how this technology can be implemented in the mortgage industry. Currently, we use some lower level rules-based programs to conduct underwriting as well as OCR usage in some back-office functions. Applying the applications discussed previously, many, if not most of the job functions being conducted today by humans could in fact be replaced with future AI programs.

One good example is the use of AI to replace loan officers for taking applications and collecting data. The Rocket Mortgage program in use today by Quicken Loans is just one example. Furthermore, most of the data collection and organization process that is labeled “processing” is also easily replaced with existing sites that offer independent validation of the information utilized in making decisions.

An area that is also ripe for AI application is the title and closing function. Using OCR, data comparison and document production, can easily be completed while the risk of mistakes or problems at the closing table could be handled in real time.

Back room operations can also be easily incorporated into AI functionality since it is very much a data recognition, document collection and validation effort. Post-closing functions which now take time and massive amounts of human labor can not only be streamlined, but the data collected can be used to revise and improve the processes themselves.

While what may appear to be already included as an AI function, underwriting is actually where some of the best deep learning artificial intelligence is applicable. Since 1995 we have been using rule-based technology to conduct what we considered AI, but instead are simply automated underwriting programs. Deep learning AI offers the industry the solution that has plagued it since its inception, that of identifying the true performance risk of loans.

Today’s credit risk function continues to use static attributes to develop, expand and or shrink credit policy without knowing the potential impact on any individual applicant. This credit risk stalemate has resulted in lending programs that reject applicants that may in fact prove to be credit-worthy borrowers. This can easily be seen in such programs as affordable housing and minority lending. Using deep learning, rather than simply applying standard credit policy to an application, AI can conduct an analysis of the applicant in comparison to all probabilities of performance and decide to approve or reject. In other words, credit evaluations would be individualized for every applicant. In addition, performance probability would be the yardstick by which pricing is tabulated.

Servicing is of course, primarily manual back-office functions that AI can address. Once again a deep learning application can contain any information on taxes, insurances and related issues, transfer funds if and when needed as well as provide an escrow analysis, tax statements and individual billing statements.

Just this brief recap easily demonstrates the value of bringing AI into the mortgage lending environment. The question is “at what cost?”   There is no doubt that the advancements in AI would have the same negative impact on mortgage lending employees. In fact, the annual convention might just turn out to be a dog and a man. However, as shown above, mortgage lending is not the only industry that will feel the same impact.

There are of course risks. One significant enough to delay implementation of AI by some firms is the risk that the technological intelligence could misinterpret input information and make decisions based on that information that would be disastrous to the company. One such example has already been experienced by Wall Street to a small degree when a mention of Anne Hathaway in the news resulted in a bump in Berkshire-Hathaway’s stock. Now known as the “Hathaway Effect”, companies are implementing practices such as running validation scenarios and are placing restrictions and stops on critical process points. This of course requires human oversight and runs headlong into the issue of AI and the reduction of human jobs in the industry.

The discussion over job elimination and creation however needs to be a much broader discussion around the impact of AI in the economy overall as well as in our industry. We have to think about what the massive reductions in employment opportunities means and what type of jobs will be created as current competencies are becoming less relevant and those trained in AI technology become harder to find. This change from the use of human intelligence to artificial intelligence is similar to the change undergone by those individuals who today are labeled “white working class” as large scale manufacturing replaced humans with more advanced technology and those individuals who were educated to do manual and factory related jobs became unemployable. Those that were smart enough to take advanced training and education in the field have found new jobs, but those that haven’t are left feeling angry and disenfranchised.

New skill sets that will be in demand for AI revolve around software engineering and data science. This is of course a given. But there will also be the need for a hybrid of business and digital skills which involves individuals who are knowledgeable about the business, who understand the digital environment and know how to benefit the business by continually improving its digital footprint.

While AI continues to advance, and becomes more accepted in the industry, it would be wise of us to think of when, where and how it becomes the most advantageous to the human side of the business equation. Using AI also means finding what our function as humans involves. Are we the masters of the technology or will we become evolve to a position seen by Steve Woznick when he said, “I now feed my dog steak because I see humans being the pets for robots and I want my robots to believe pets eat steak.”

About The Author

How To Use Artificial Intelligence

The use of artificial intelligence applications in business is growing, but AI and machine-learning aren’t yet an efficient use for every business task, according to an infographic.

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Published by LatentView, a marketing automation and digital analytics platform, the infographic details what criteria make a task a good candidate for AI, and goes on to explain how to use AI in your business.

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For example, in marketing, decisions about personalized offers to customers may work well with AI, but decisions about marketing strategy are best left to the humans (at this point, anyway), the infographic says.

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In customer service, AI can learn to improve responses based on historical chat data, but decisions that are based on empathy still require human intervention.

To learn more about when AI can and should be used in business, check out the infographic:

TLI317-Data Image

The AI Era Is Here – Pt. 2


In the latest issue for Fortune, Erin Griffith examines the investment trends in AI (Artificial Intelligence) technology and poses the question: is AI an overhyped fad or a revolution? She writes, “There’s an easy way to tell when the hype around a technology trend has peaked. 1) Are the smartest venture capitalists complaining about valuations? 2) Are big tech companies snapping up start-ups so young they can barely be considered real businesses? 3) Are Fortune 500 executives talking about their [insert trend here] strategy? If the answer to any of these questions is yes, congratulations! You’ve identified a fad.”

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Of course, most revolutions look like fads in their early days—because they are. Distinguishing between those fads that will fade and those that will become the norm in the long term can be difficult, and as in all things, hindsight has a much higher success rate than foresight when it comes to identifying the winners and the losers. So what data might guide such an evaluation?

The research firm CB Insights recently reported that in 2016 there were 658 venture capital deals in the AI sector. In 2016, that amounted to $5 billion in startup funding deals, a significant increase from $589 million in 2012. International Data Corporation projects worldwide revenue from artificial intelligence and cognitive systems to be $47 billion in 2020, up from $8 billion in 2016.

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CB Insights selected 100 of the most promising artificial intelligence startups globally from a pool of 1,650 candidates based on factors like financing history, investor quality, and momentum. A look at the top 50 shows that AI is surging worldwide with 20% located outside the United States. They cover a wide range of market segments: core AI, FinTech, auto, health care, commerce, CRM, cyber-security, robotics, business intelligence, and text analysis and generation.

Interestingly, the fact that AI does not necessarily intersect with established business cases has not proven to be a hurdle to investment. “These are not businesses,” says John Somorjai, executive vice president of corporate development at Salesforce, which has acquired a handful of AI companies. “These [deals] are about technology and talent.”

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The development of artificial intelligence has inspired both fascination and dread.

In 1955, the term AI represented the concept of autonomous systems modeled on the structure of the human brain. At the same time, other researchers were tackling a different problem: finding patterns in what was then considered great volumes of data and making proper selections, or decisions, based on that data. In 1956 William Ross Ashby wrote in his Introduction to Cybernetics that “…what is commonly referred to as ‘intellectual power’ may be equivalent to ‘power of appropriate selection’.” This was not intended to as “artificial intelligence” in the way we typically understand it, and in fact was labeled as the inverse: IA, or Intelligence Augmentation. If this model sounds suspiciously familiar, it is because today’s AI systems are constructed on the IA paradigm. Our real-world applications, including language processing, machine learning, and human-computer interaction are based on IA—data pattern recognition and appropriate decision making—and as such, they augment our capacity to understand what is happening in the complex world around us. While the term “AI” became the label of choice for such technology, it is an ironic misnomer.

Let’s look at the “Why You Should Let Artificial Intelligence Creep Into Your Business” article in the March, 2017 issue of Inc magazine for some definitions:

How AI works: problem solving: Unlike traditional computing, which delivers precise solutions within defined parameters, AI, sometimes referred to as cognitive computing, teaches itself how to solve problems. “Instead of delivering specificity, AI-centric programming generates millions of solutions, evaluating each for efficacy and then choosing the most viable and optimal ones,” says Amir Husain, CEO and founder of SparkCognition.

What it does better: data diving: Manually finding your target customer, by searching and poring through income-level, interest-based, and geographical data, is labor-intensive and time-consuming. AI cuts to the chase. “For example, using a feed of three key pieces of information that the entrepreneur provides; a brief product description text, images and a price range; an AI system can zip through social media and other online outlets, looking for correlations between product and digital conversations,” says Husain. If you give it the green light, AI’s natural language processing technology then writes and sends a sales pitch, notes transmission times, and analyzes feedback. “You can almost hear an AI system going, Aha! I’ve cracked the code.” says Husain, adding that AI constantly optimizes itself by making slight changes to the message.

Where it works: practical apps: One key reason for AI’s upsurge is entrepreneurs’ free or inexpensive access to libraries such as IBM Watson, Goggle TensorFlow, and Microsoft Azure. These application programming interfaces (APIs) allow coders to build AI apps without starting from scratch. Husain expects to see a proliferation of AI-centric marketing, sales and other service startups focused on small and medium-size businesses.

Let’s look at some specific examples from the same article.

Call Centers: The biggest misconception about AI is that it’s robots with human faces sitting at remote desks. “AI is nothing more than an add-on technology, spice and flair, to an otherwise conventional system, such as a traditional travel-reservation site that, because of AI can now converse with a human,” says Bruce W. Porter, an AI researcher and computer science professor at the University of Texas, Austin. Porter emphasizes that future breakthroughs will not be 100 percent AI. “AI will likely provide a 10 percent product or service performance boost,” he says. That is, in fact, huge. Firms that fail to make the leap, he says, may fail to have customers.

Information Retrieval: Not all searches are as simple as typing a few keywords and having Google take over. Entrepreneurs often need more in-depth and complicated excavations for patent and trademark data, for example and that, in turn, involves an often-hefty legal budget to pay a highly-trained human to do. Porter foresees within five years many companies offering services to consumers who have no experience in AI or specific knowledge fields. They’ll be able to conduct their own AI based data retrieval. Count on industry disruption, he says, as this type of AI application will leapfrog current data-retrieval-service providers.

Contract Generation: Because it’s able to generate natural language, AI is an exceptional tool for helping entrepreneurs assemble contracts, as opposed to buying them off the shelf at, say LegalZoom. AI applications will converse with – by text and, ultimately, voice – and tease information out of humans that will become components of formal agreements, such as details about fee payments and product returns. Porter anticipates users will pay to access cloud-based AI computer systems to produce such documents. AI-centric startups, because they don’t require a human in the loop and won’t need to hire staffers, can offer their services at a very low cost, especially given an anticipated large volume of customers and business competition.

AI can displace humans, but it can’t replace them.

Leaders of every industry and institution are sprinting to become digital. Who will win? The answer is clear: It will be the companies and the products that make the best use of data. And the ones that make the best use of data will likely be the ones that use AI to gain efficiencies in data analysis and decision making.

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The AI Age Is Here


Artificial intelligence has gained prominence recently due, in part, to big data, or the increase in speed, size, and variety of data that businesses are now collecting. Artificial intelligence, or AI, can perform data-related tasks with great efficiency, and it can identify patterns in the data that often eludes human analysis. As organizations strive to gain more insight from their data, it’s not surprising that the business world is looking to AI for a competitive edge.

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It’s not like this is the latest and greatest innovation! The term artificial intelligence—an umbrella concept that encompasses everything from robotic process automation to actual robotics—was coined in 1955 by John McCarthy, an American computer scientist, and it gained traction in the academic community at the Dartmouth Conference the next summer. As Daniel Crevier describes it in his book Ai: The Tumultuous History of the Search for Artificial Intelligence:

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In the summer of 1956, ten young scientists, some barely out of their doctoral studies, sat down to consider the astounding proposition that ”every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it.” Armed with their own enthusiasm, the excitement of the idea itself, and an infusion of government money, they predicted that the whole range of human intelligence would be programmable within their own lifetimes. Nearly half a century later, the field has grown exponentially – with mixed results.

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By the early years of the 1980s, a consensus was forming that expert systems were the future of artificial intelligence. An expert system is a computer system that mimics the decision-making skills of a person. It makes sense in theory: feed enough data to the system to create the proficiency of a human expert, and you can theoretically get human-like decisions from it. Unfortunately, such systems are prohibitively expensive to develop and have only proven to be useful in targeted scenarios. In many respects AI has demonstrated a wide scope, but shallow influence: it has touched countless disciplines, but its impact has been limited to the most simple form of call-and-response interactions.

Today’s AI research and development focuses on artificial neural networks: systems duplicating the interconnected process of the human nervous system. AI can combine the reasoning ability of the human mind with the processing power of computers, such as in Apple’s Siri personal assistant and Amazon’s Alexa. A recent article in the Wall Street Journal stated, “Spending on AI technology is expected to grow to $47 billion in 2020 from a projected $8 billion this year, according to market-research firm IDC.”

As a consequence, some business executives are working to become familiar with methods of managing the development of applications and the design of algorithms across multiple lines of business. Brian Uzzi, a professor at Northwestern University’s Kellogg School of Management, has co-developed three AI courses for M.B.A.s. In April 2017, Kellogg plans to introduce Human and Machine Learning, a 10-week elective course. The broader objective, according to Mr. Uzzi, isn’t to create a cadre of engineer-executives, but to introduce future corporate leaders to the idea of making decisions with the help of machines. Artificial intelligence is now on the syllabus at top-tier business schools.

A recent MIT Technology Review looked at a major report from Stanford University, coauthored by more than twenty leaders in the fields of AI, computer science, and robotics and concluded that AI looks certain to upend huge aspects of everyday life, from employment and education to transportation and entertainment. The analysis is significant because public alarm over the impact of AI threatens to shape public policy and corporate decisions.

The report predicts that automated trucks, flying vehicles, and personal robots will be commonplace by 2030, but it cautions that remaining technical obstacles will limit them to certain niches. It also warns that the social and ethical implications of advances in AI, such as the potential of unemployment in certain areas and likely erosions of privacy driven by new forms of surveillance, will need to be open to discussion and debate.

In December 10, 2016, Andrew Tonner published the 9 Artificial Intelligence Stats That Will Blow You Away.

1.) Voice assistant software is the #1 AI app today: Many of these voice-powered AIs still leave something to be desired in terms of accuracy, and it was surprising that voice assistants outnumbered big data in overall popularity with businesses.

2.) AI bots will power 85% of customer service interactions by 2020: Bye-bye, call centers and wait times. According to researcher Gartner, AI bots will power 85% of all customer service interactions by the year 2020.

3.) Digital assistants will “know you” by 2018: Also from Gartner, digital customer assistants will be able to “mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, timing and tone, and the ability to respond, add to and continue with a thought or purpose at multiple occasions and places over time.”

4.) Amazon, Alphabet, IBM, and Microsoft to host 60% of AI platforms: These 4 tech giants already have significant cloud computing businesses, a trend researcher IDC sees as likely to continue and by the start of the next decade, will control most of the market for AI software applications.

5.) Get excited for self-driving cars: According to a study from leading consultancy McKinsey, the impact of self-driving cars will be tremendous, saving an estimated 300,000 lives per decade by reducing fatal traffic accidents. This is expected to save $190 billion in annual critical care and triage costs.

6.) 20% of business content will come from AIs by 2018: In a potentially apocalyptic turn for members of the media reading (or writing) this, AI-powered software will write as much as 20% of business content in a mere two years’ time according to Gartner.

7.) AI drives a $14-33 trillion economic impact: In a research report to its investors, Bank of America argued that the rise of AI will lead to cost reduction and new forms of growth that could amount to $14-$33 trillion annually, in what it calls “creative disruption impact,” and that’s just the tip of the iceberg in some experts’ view.

8.) Robots will be smarter than humans by 2029? According to Alphabet director of engineering Ray Kurzweil, machines will be smarter than us by 2029. Kruzweil doesn’t necessarily see this as being a negative, though. Among many other “bold” predictions about our AI-laden futures, he believes people will start living forever around the year 2029 as well. Whether that’s the result of some Matrix-like scenario coming to fruition isn’t immediately clear, but obviously leading experts in the field believe major changes to our social fabric are only a little more than a decade away.

9.) Zero people actually know how big an impact AI will have: While it’s certainly easy to get wrapped up in the litany of predictions, it’s perhaps most useful to simply keep in mind that AI should have a major economic impact from which investors can undoubtedly benefit from today.

The one concrete takeaway is that AI will contribute to the rapidly shifting technology landscape for our industry. Organizations that want to get or stay ahead will be flexible adapters who are willing to evolve their operations to take advantage of AI-based tools that enhance the customer experience, streamline internal processes, and feed the business pipeline.

Summary: Artificial intelligence (AI) is all around us – we encounter it in our daily tasks, such as talk-to-text and photo tagging, and it is contributing to cutting-edge innovations such as precision medicine, injury prediction, and autonomous cars. AI is the next big revolution in computing and holds the promise to provide insights previously unavailable while also solving the world’s biggest challenges.

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Get Personal To Ensure Success

The mortgage industry is all about relationships. So, when you are marketing, you have to make it personal. The pairing of Big Data and technology improvements has helped marketers get better at personalizing communications, says an infographic from Open Topic, an artificial intelligence platform.

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Although challenges in using data in an efficient and effective way still exist, technological improvements such as natural language processing and auto-publishing can help marketers navigate this territory, the infographic points out.

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And navigating that territory is worth it, as 66% of marketers say the main drivers of personalization are better customer experience and improved business performance, according to the infographic.

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With more personalized experiences leading to better, more loyal customers, there’s no reason not to be on board.

To find out more about how to make personalization a top priority for you, check out the infographic:

TLI1216-Data element

Robots And AI Invade Banking


From Rosie the Maid, a humanoid robot fictional character from the 1960’s animated TV series The Jetsons, to Fritz Lang’s Metropolis and Isaac Asimov’s ‘I, Robot’ and even WALL-E, C-3PO, Optimus Prime and R2-D2, robots have always fascinated and entertained consumers. But, in real life, they have been far less entertaining (or functional).

Sure, they have become a part of every factory room floor in manufacturing and have played major roles in space exploration and taken on difficult and dangerous tasks, but until recently, robots have not proven to be nearly as intelligently evolved (or financially viable) as projected.

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That is all changing. The exponential growth in the power of technology, digital sensors and information processing has improved the potential of robots at the same time as the innovation and investment in these devices is taking off. Both businesses and consumers can benefit from the rise of the robots.

While much focus is placed on making smart people smarter, the leading benefit of robots and artificial intelligence (AI) processes today is to standardize delivery followed by improved domain expertise and skills as subject matter experts, including language capabilities.

Robots and AI in Banking

The primary opportunity for robots and AI tools in the banking industry at this time is that they can extend the creative problem-solving capabilities and productivity of human beings and deliver superior business results, states Cognizant in a report on the use of this new digital technology. Their research shows that through these technologies, humans have the potential of attaining new levels of process efficiency, such as improved operational cost, speed, accuracy and throughput volume.

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The opportunity for cost savings is the first place where AI and process automation will impact banking. In the Cognizant study, 26% of banking respondents stated they have enjoyed 15%-plus cost savings from automation in their front office and customer-facing functions compared with one year ago, and 55% expect those same levels of savings (15% or more savings) within the next three to five years.

According to Cognizant, the top drivers for automation beyond cost savings include:

>> Reduced error rates (21%)

>> Better management of repeatable tasks (21%)

>> Improved standardization of process workflow (19%)

>> Reduce reliance on multiple systems/screens to complete a process (14%)

>> Reducing friction (11%)

Cognizant found that nearly half of the banks surveyed (45%) have also seen at least 10% revenue growth from analytics aligned with their front office and customer-facing functions, a number that is anticipated to rise to nearly three out of every four banks during the next three to five years. The result is that banking is more inclined than other industry surveyed to automate their processes, often due to their need to better focus on customers.

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While process automation and the processing insight from transactions can impact all areas of the banking organization, including human resources, finance and accounting, customer service and even new product development, the impact of FTEs is projected to be significant. In fact, 19% of banks surveyed by Cognizant believe there can be a 25% FTE reduction today, with 28% believing a 25% reduction in FTEs will be possible in the next 3-5 years.

So what jobs may be at risk? As noted in a recent post by futurist and best-selling author, Brett King on the future of AI in banking, a study released by Oxford Martin School’s Programme on the Impacts of Future Technology evaluated how susceptible are jobs to computerization. Evaluating around 700 jobs, and classifying them based on how likely they are to be computerized, the jobs in the financial services industry that fit the studies criteria include:

>> Bank Teller

>> Loan Officer

>> Mortgage Broker

>> Insurance Claims and Policy Processing Clerk

>> Insurance Underwriters

>> Claims Adjusters, Examiners and Investigators

>> Bookkeeping, Accounting and Auditing Clerks

>> Tax Preparers

When asked about the biggest challenges associated with efforts to digitize processes, executives across industries say that data security is the biggest issue they confront, now and in the future. Fifty-two percent of respondents to the Cognizant survey indicated that data security is the chief challenge today.

It is believed that as digital processes proliferate, and as leaders see the value they create, an entirely new ecosystem of value-added services will develop to ensure the security, risk, privacy and compliance of the value chain of information these processes generate.

Nao and Pepper Entertain and Serve Banking Clients

As a ‘living’ example of how robots can be utilized in financial services, Bank of Tokyo-Mitsubishi UFJ took a first step toward employing non-human staff in April, with the introduction of a customer service humanoid robot at its flagship Tokyo outlet. Standing 58 cm tall and weighing 5.4 kg, the Nao robot worked at the reception area, according to Mitsubishi UFJ Financial Group Inc.

The robot, named Nao, was developed by French company Aldebaran Robotics, a subsidiary of Japanese telecom and technology giant SoftBank Corp., speaks Japanese, English and Chinese and was thought to be the first among the world’s major financial institutions to employ a customer-facing robot.

The robot uses various gestures and analyzes facial expressions and behavior to provide context appropriate responses to customer questions. It operates in 19 languages, offering the bank significant opportunity to expand the language coverage should the robot service take off.

While the robot is not intended to replace branch workers with a robot, they are being used to meet and greet customers, answering simple questions with various languages, freeing up some of the branch staffs’ time to work on more value added services. High-definition cameras record and match different customers, so identification can begin as soon as the customer steps through the door of the branch.

“Currently, in a lot of branches, there are cases where quite a few customers don’t speak Japanese or English and so we can have Nao do an initial check (on their needs) so that it can lead to a trouble-free setting up of an account or other carrying out of other administrative issues,” said Tadashi Betto, Bank of Tokyo-Mitsubishi UFJ Chief Manager of eBusiness and IT Initiatives Division.

Using stored insight, the robot routes the customer to the appropriate person based on past experience, products utilized or current mobile activity. Nao uses each interaction to learn a customer’s preferences and personality which enables the robot to increase the accuracy of each subsequent interaction.

Nao lasts 12 hours between charges, costs approximately $8,000 and can remember details from more than 5.5 million customers and over 100 different products. While there are limits to Nao’s capabilities (currently), this is both cheaper and more product conversant than any human in the same role.

Meanwhile, local competitor Mizuho Bank also plans to use a robot to assist customers in the next few months. Mizuho will use Pepper, Nao’s big brother, at several of its branches in much the same way as Bank of Tokyo-Mitsubishi UFJ.

Pepper, roughly twice the size of Nao, could become the first humanoid consumer robot and the beginning of an era of mechanized, cloud-connected ’emotion-reading’ digital assistants. Having gone on sale to the public in June for roughly $1,600 (plus data charges), the robot communicates in multiple languages, using sensors and cloud-based artificial intelligence (AI) capabilities and having the ability to evolve its skills over time through ongoing learning.

“Thanks to its ’emotion engine,’ Pepper can recognize human feelings and simulate them. It can also learn new skills as it spends more time with users and connects through the SoftBank cloud to thousands of other Peppers,”says its developers.

Robots Invade the U.S.

Sterling Bank & Trust in California have introduced two robots as greeters at the bank’s new locations opening in Cupertino and Alhambra, in the Los Angeles area. As part of their ‘training,’ the two robots made appearances at the bank’s San Francisco branches.

More a novelty than providing any significant off loading of duties during the initial use at Sterling, the robots are highly popular with children and grandchildren, said Steve Adams, senior vice president of Sterling Bank & Trust. The robots demonstrate kung fu moves and dancing while also greeting customers and handing out bankers’ business cards.

Hello Watson

IBM previously announced that its Watson-based chat advisor application built banking is being adopted by banks and other financial institutions for customer service and scaling wealth management. Genesys, a customer service company, will use IBM’s Watson system to better handle its clients’ customers’ needs. Banks are the service’s first clients. This same system is being used with the Pepper robot implementation discussed above.

IBM also sees artificial intelligence playing a big role in bringing wealth management to the masses. The intention is to take the expertise of wealth advisors and build it into a system so that people can interact with the system to get the first parts of the wealth management conversation handled. While using AI for wealth management goes beyond simple Q&A applications, Singapore development bank DBS as well as the Australian bank ANZ are already developing wealth management applications that are based on Watson.

“The goal is to take basic customer service and the wealth advisor to scale. Robotics are going to handle client interaction that doesn’t have to be face to face,” said Mike Rhodin, senior vice president of IBM’s Watson’s group. For more on robo-advising, Chris Skinner has discussed this well on his Financial Services Club Blog.

Barclays also made an announcement around the use of robot technology to make money transfers and to perform other rudimentary tasks. An artificial intelligence (AI) system similar to Apple’s iPhone personal assistant Siri may be used so that people will be able to talk to a device and receive the information they ask for.

“We’re very soon going to be entering a world where we may not have to be physically touching a device in order to execute transactions or to be able to engage with computers,” Derek White, chief design and digital officer at Barclays, told CNBC in an interview at London Technology Week. It is thought that Barclays could potentially design apps that integrate with such robot systems allowing users to do banking by talking to their mobile devices.

Preparing for the Robot Revolution

According to David Brear from Think Different Group, robots as the replacement for humans is a ways off. On the other hand, he emphasizes that, since the best customer branch experience is based on a deep understanding of the consumer, their situation and a good degree of empathy, artificial intelligence could definitely help in supplementing current, somewhat misguided and frustrating branch experiences.

“The better use of data and use of AI to standardize the decision making process should lead to every customer interaction being with empowered staff at the very top of their game. So rather than replacing them with an ‘android,’ we are heading towards improved technology and data facilitated discussions and meetings.”

Robotic process automation with sophisticated technologies is here to stay. Humans will remain essential to how knowledge work is orchestrated and managed, but technologies can now create more effective knowledge workers while simultaneously generating and capturing data that can improve processes and eliminate wasteful steps.

One of the insights of Asimov was that it is easier to ask such questions when the technology is more human-like. With this in mind, robots could serve as the collectors of new insights and perspectives – as people look at them and see them looking back … with some form of automated understanding of their needs, temperament and behavioral tendencies.

Start today, imagine how the future of work will look when digital machines, information and processes help humans do their jobs better, faster and with greater impact. By automating systems and interpreting data and insight, robots have the potential to work side-by-side with humans, allowing them to serve customers more effectively.

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Things To Ponder: Artificial Intelligence And Computer Programming

*Artificial Intelligence And Computer Programming*
**By Roger Gudobba**

***Last time I talked about artificial intelligence having an impact on creative computer programming in the early 1960s. Let’s explore that further. In those first years the majority of computer systems were devoted to scientific number crunching, performing accounting functions or some other specific mathematical task. I was attending college and was fortunate to get a part-time job with a psychiatrist analyzing data for a research project. He was using a Bendix (I bet you thought they made brakes) G-15 computer. He was very frustrated with the time and effort required to get results. I had absolutely no idea what to do but looked at this as a great opportunity.

****What does this have to do with artificial intelligence and computer programming?

****One of the first programs I developed was used to guide the psychiatric residents’ thought process in determining the correct diagnosis for a patient. I presented questions and then modifying the sequence of questions based on user responses. I didn’t realize it at the time, but such an application might be considered a “rules-based decision engine” today.

****The program was modeled after ELIZA, a computer program and an early example (by modern standards) of primitive natural language processing. ELIZA was written at MIT by Joseph Weizenbaum sometime between 1964 and 1966.

****ELIZA has almost no intelligence and instead mimics the observable signs of human intelligence with tricks like string substitution and canned responses based on keywords. Nevertheless, when the original ELIZA first appeared in the 60s, some people actually mistook her for human.

****Simply getting a text sequence into a computer is not the same as imparting on it an understanding of natural language, and programming it to parse sentences is not the same as giving it the power to converse. The computer must be provided with a precise understanding of the domain to which the text relates, and this is possible for very limited domains.

****I was working at the Lafayette Clinic, a research and training facility connected to the Wayne State University School of Medicine and the Michigan Department of Mental Health. The clinic was recognized as one of the top three research facilities in the world for the study of schizophrenia. That reputation was gained in part by the fact that we had the largest and most complete collection of published articles on schizophrenia. The psychiatric residents read and cataloged articles and identified keywords. Researchers from around the world would request information based on different criteria and the computer program would read a tape file and list the article number that matched the criteria. We would then copy the articles and distribute them to the researcher making the information request. I developed this application based on a product from IBM called KWIC (Keyword in context). It sounds a little primitive based on what you can do today with online search engines, but remember that every antiquated process was once an innovative step forward.

****I was very passionate about the work and felt we were making strides in identifying the cause, effect, and potential treatments for mental illness. We were making a difference.

****During my 18 years there, however, the environment slowly changed. Technology was rapidly evolving and the public sector struggled to keep pace. Research grants became fewer and scientists fought to get funding. The state hospitals in Michigan were downsizing or closing, in part from cost cutting, in part from the desire to decentralize mental health care. Along the way, public policy shifted to allow psychiatric patients greater rights and responsibilities in determining their own course of treatment. I don’t disagree with that initiative, but the pendulum swung too far in the other direction. Now too many people who need help are on the street. I was discouraged and left to start a computer consulting firm specializing in working with small businesses. Eventually, this led me to the mortgage industry.

****Based on my experiences I came to ask: can computer programming ever replace the human intelligence factor? What do I mean? For example, when you think of a bird you immediately picture the bird flying. But then you think of the Penguin, which is a bird that doesn’t fly. Throughout my career I realized that computer programming was a tool to solve problems. But the key concept as I have always seen it was to understand the problem you were trying to solve before you attempted to develop the solution.

****Next time I will address some specific mortgage solutions.