[In his column last month (May 2018), Randy explored the “Why and How of Artificial Intelligence,” with a focus on the positives and negatives of AI for accounting firms and professionals.]
From the June 2018 Issue.
Artificial Intelligence has matured from technology buried in computer science labs using complex coding techniques to more common algorithms and supporting technologies used as part of the design strategy of new generation products. Many of the developers have known of AI techniques for years but did not have a practical way to apply the algorithms because the compute overhead was too high, the sample of data was too small and the number of techniques that needed to be applied made the code too complex. With centralized computing in SaaS applications and cloud data centers, AI has become much more practical and accurate.
So how do Artificial Intelligence approaches work? They use:
- Cybernetics and brain stimulation – connection to neurology .
- Traditional symbolic AI – John Haugeland named these approaches to AI “good old fashioned AI” or “GOFAI“ exploring the possibility that human intelligence could be reduced to symbol manipulation.
- Cognitive simulation – Economist Herbert Simon and Allen Newell studied human problem-solving skills from psychological experiments resulting in the Soar architecture in the 1980’s.
- Logic-based – John McCarthy in his laboratory at Stanford (SAIL) used formal logic and led to the Prolog language and the science of logic programming.
- Anti-logic or scruffy – Marvin Minsky and Seymour Papert found that solving difficult problems in vision and natural language processing required ad-hoc solutions.
- Knowledge-based – led to the development in the 1970’s of expert systems, introduced by Edward Feigenbaum of Stanford.
- Sub-symbolic – when traditional symbolic AI stalled in the 1980’s unable to solve problems in perception, robotics, learning and pattern recognition, researchers tried to not encode knowledge.
- Embodied intelligence – Researchers of robotics, such as Rodney Brooks, reintroduced the use of control theory and embodied mind.
- Computational intelligence – neural networks and “connectionism” was revived by David Rumelhart leading to soft computing approaches including fuzzy systems, evolutionary computation and statistical tools.
- Statistical methods – sophisticated mathematical tools to solve specific subproblems that are truly scientific, in the sense that their results are both measurable and verifiable.
- Intelligent agent – a system that perceives its environment and takes actions which maximize its chances of success.
What does artificial intelligence mean to the practice of accounting and to accountants? We have several working examples available:
- Accounting – Zoho Zia (Zoho Intelligent Assistant) workflow analysis, best time to contact, automatically research and suggest data to complete client records, email sentiment analysis.
- Auditing – Mindbridge.
- Financial services – Kasisto, Moneystream.
- Sales – Salesforce PredictionIO.
- Self-writing applications – Crane.ai.
We see a number of products that are running crude AI today and vendors that are pretty far along in their use of AI. Getting products that apply to small and medium businesses are more of a challenge, but we see efforts at Intuit, Xero, BQE Core, Citrix ShareFile, Thomson, CCH and most other products that apply to the CPA profession and to small and medium businesses.
As development continues and Artificial Intelligence transitions from an emerging technology to a mainstream technology, vendors will choose from many open source and proprietary suites that have Artificial Intelligence capabilities or they will develop their own algorithms inside their products. Examples today include:
- Ayasdi
- Azure Machine Learning Studio
- Cloud Machine from Oracle
- Crane
- Cyc
- Deeplearning4j
- Playment
- TensorFlow
- Theano
- Torch
- A list of 15 tools which includes comments and some of the above can be found here
- Another good list of AI tools can be found here
The best example of tools for accounting that are working today is:
- IBM Watson – this tool has been configured for analysis of tax, as well as analysis of core financial and operational data by KPMG
- SAS – Visual Investigator cost $1B to develop. David Stewart, Director, Financial Crimes & Compliance of SAS, suggests 50-70% of banks’ compliance spend is on AML. Financial firms spend 4% now and will spend about 10% in 2022.
- Zoho Zia (Zoho Intelligent Assistant) workflow analysis, best time to contact, automatically research and suggest data to complete client records, email sentiment analysis
Continue reading online at: www.cpapracticeadvisor.com/12407031
Here’s a summary of what you need to know about Artificial Intelligence:
|
Key Information |
TECHNOLOGY: Artificial Intelligence |
|
Why is the new technology better? |
It is a method of data analysis that automates analytical model building |
|
How can you do this today? |
|
|
Risks |
Wrong data set, conclusion unguided |
|
Where/when to use |
When data can answer a specific question |
|
How much? |
Can be up to $10K per hour, or free on open source |
|
When expected in mainstream |
Simple AI now, fake AI common in current promotions, usable AI 4-6 years |
|
Displaced technology or service |
Repetitive or analytical human labor |
|
Other resources |
Recommended Next Steps
Watch for applications that claim they have Artificial Intelligence. Do the applications truly exhibit learning? Are they rules or forms based and limited? If so, they may not be AI. Do they improve over time? Does more data make them more accurate? Can the applications make new conclusions without additional programming? If so, they may be truly be AI?
Just like Machine Learning (covered in an earlier article in this series), what you are trying to filter out is products based on rules, forms or pattern recognition that is programmed to recognize each specific form/task and make a decision based on recognizing the form or task. You want the system to accept inputs of all kinds, recognize new data, learn about the data, and make conclusions that provide insight. Like the human race, it is hard to predict where AI will take the capabilities of machines and computers. As many of you have heard me say before, computing can be used for good or bad. I tend to look at the bright side of life as we are reminded by Monty Python here.
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