AI and the CPA: OpenAI's ChatGPT will Blow Your Mind! Try it Now!

We previously discussed OpenAI in this post. In that post, I closed with this comment:

"We should be aware of the concept of exponential change. Technology, like AI, improves at an exponential rate and not a linear rate. Consequently, monitoring the space is key for CPAs and other to ensure that they see change coming and adapt accordingly."

And so, my fellow CPAs, you need to check out how much AI has advance in the two years since that post. It's absolutely amazing to see how advanced the technology is and how quickly it has come:

YouTube is one thing. But testing it out is another. To be honest, I was about to review the video and then summarize it. Then I thought, why not ask ChatGPT? 

Question to ChatGPT: "What are 5 key takeaways for CPAs with respect to ChatGPT?"

Answer: 

1. ChatGPT is a large language model trained by OpenAI.
2. ChatGPT has a knowledge cutoff of 2021, and it does not have access to the internet or current events.
3. ChatGPT is able to generate human-like responses to a wide range of questions, but it is important to keep in mind that it is a machine and may not always provide accurate or complete information.
4. ChatGPT is not a substitute for professional advice or guidance, and it should not be used as the sole basis for making important decisions.
5. ChatGPT is intended for general informational purposes only, and it is not a substitute for professional judgment or expertise. It is important for CPAs to consult with appropriate sources and to exercise their professional judgment when making decisions.

Reading through the takeaways, it seems like it added an extra dash of risk management. Could it have accounted for our risk-aversion?

Then I thought, why not ask it directly about the video itself? 

Question to ChatGPT: "What are 5 key takeaways from It’s Time to Pay Attention to A.I. (ChatGPT and Beyond)"

Answer:

1. Artificial intelligence (AI) is a rapidly developing field with the potential to significantly impact many aspects of society, including the accounting profession.
2. AI technologies, such as chatbots and language models like ChatGPT, are becoming increasingly sophisticated and can be used for a wide range of applications.
3. It is important for CPAs to stay informed about the latest developments in AI and to consider how these technologies may impact their profession and their clients.
4. While AI has the potential to improve efficiency and accuracy, it is important to carefully consider the risks and limitations of these technologies, including the potential for errors or biases.
5. CPAs should consider ways to incorporate AI into their practices, such as by using AI tools to automate certain tasks or by providing guidance to clients on the use of AI. However, they should also ensure that they have the necessary skills and knowledge to effectively use and understand these technologies.

Notice how I did not have to tailor this questions for CPAs? ChatGPT automatically brought the context over from the previous question.  (As for how good this summary is, you can judge for yourself.)

Then, I decided to ask it about how exponential technologies will impact the CPA profession. 

First question, "Will blockchain replace the need for financial auditors?"

Answer:

"Blockchain technology has the potential to revolutionize many aspects of the financial industry, including auditing. However, it is unlikely that blockchain will completely replace the need for financial auditors.

One of the main features of blockchain is its ability to provide a tamper-evident record of transactions. This can be beneficial for auditing purposes, as it allows auditors to more easily verify the accuracy and completeness of financial records. However, auditors still need to be able to interpret and analyze the data contained in these records, and to ensure that the data is consistent with other information and documentation.

Additionally, while blockchain can provide a secure and reliable record of transactions, it is not a substitute for the expertise and judgment of trained financial professionals. Auditors bring a deep understanding of financial principles and practices, as well as the ability to identify and assess risks and potential areas of concern.

Overall, it is likely that blockchain will augment and enhance the role of financial auditors, rather than replacing them entirely."

Notice the nuanced response? It should have spoke to validity, instead of completeness when it came to how the blockchain will assist the auditor. However, it didn't just list all the audit objectives just "to be safe". 

Then I asked about artificial intelligence:

Question: "How can AI be used to automate financial auditing?"

Answer:

"Artificial intelligence (AI) can be used to automate certain aspects of financial auditing, such as the analysis and interpretation of financial data. This can potentially improve the efficiency and accuracy of the audit process.

For example, AI algorithms can be trained to analyze large amounts of financial data and identify patterns and trends that may not be immediately apparent to human auditors. This can help auditors to more quickly and effectively identify areas of potential risk or concern.

AI can also be used to automate the testing of transactions and the evaluation of internal controls. For example, an AI system could be trained to perform automated tests on transactions to ensure that they are recorded correctly in the financial records. This can help to reduce the time and resources needed to perform these tasks manually.

Overall, the use of AI in financial auditing can help to improve the efficiency and accuracy of the audit process, while also freeing up auditors to focus on more complex and higher-level tasks that require human judgment and expertise."

Recognize the theme of "race with the machine" in both this response and the previous blockchain response? This is especially integrated into the last paragraph, which speaks to what AI can do versus what "human judgment and expertise" can do. Also, notice how it explains AI role in both risk assessment and testing of internal controls. That is, it had enough where "understanding" to breakdown the response into different aspects of the audit. 

When looking at this, we need to ask ourselves: is this inflection point for exponential rise of AI? 

Do review the last part of the video, where Samuel H. Altman, CEO of OpenAI, explains how he anticipates the impact of such technology on the legal profession. (The video earlier takes about how Josh Browder's Do Not Pay, will leverage the tech. I had previously seen Browder on a panel in 2016; see this post for the video). 

It's not quite a stretch to apply what he says to the CPA profession. There are key differences; in that we opine on financial statements, provide tax advice based on financial data, and the like. However, audit data analytics tech has been around for decades. It's just a matter of getting the different parts to talk to each other. 

Clearly, it's early days for ChatGPT and many issues need to be sorted out. For example, it has already earned the moniker "CheatGPT" for how it can be potentially used as a short-cut by students. That being said, it's clearly the biggest watershed moment for AI and the white-collar workforce, since IBM's Watson defeated Ken Jennings and Brad Rutter. 

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

AI and the CPA: What should CPAs know about GPT3?

In the epic battle between man versus machine, the chess champion Gary Kasparov threw the match because it is alleged that he thought that the AI-powered system did a well-calculated move. In reality, it was just a random move that the system threw out because it had experienced a technical glitch.

One up-and-coming AI-enabled service to watch is GPT-3 from an organization called OpenAI. OpenAI was started by Elon Musk. It’s in beta right now and in a really raw state but the capabilities that have surfaced are pretty amazing. 

One capability (as noted in the video) it has is summarizing long reads. However, there are serious flaws that need to be worked out. For example, it advised a fake patient suffering from depression to kill themselves. So it's not going to be rolled out at a hospital any time soon, but it is something definitely watch.

AI and CPAs: Competitors or Collaborators?

AI could make the profession more sustainable, as these mundane tasks could be handed to a system. MIT’s Eric Brynjolfsson describes this concept as "race with the machine". The idea is that doctors, accountants, lawyers, can work better together with technology. It’s almost like a second set of eyes or someone that can help you assess whether the professional judgement on an issue is correct.

However, this is not something that is currently on the horizon. 

What’s more realistic is understanding where the economics of automation will apply for more basic things like have a more timely close. McKinsey put out a study in August 2020 that found automating and increasing the accuracy of forecasts helped management make better decisions. One case study they highlighted was a manufacturer that was able to reduce inventories and product obsolescence by 20 to 40 percent. 

Change is coming faster than we expect

We should be aware of the concept of exponential change.  Technology, like AI, improves at an exponential rate and not a linear rate. Consequently, monitoring the space is key for CPAs and other to ensure that they see change coming and adapt accordingly. 

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

'The Algorithm Made Me Do It': How Racist-Tech led an African-American man sleeping in a filthy cell

We've heard of Fintech, maybe even Regtech, but have we heard of Racist-Tech?

In the past few weeks, the US sees the largest protests in its history. I am not referring to the protests where armed protestors show up to state-capitals without much reaction. Rather, these are the protests that were in response to the death of George Floyd. George Floyd who died after a police officer kneeled on his neck (with his hands in his pocket) for eight minutes and forty-six seconds. These protests, in contrast, have been met with a strong reaction.

A related incident occurred a few months before Mr. Floyd lost his life.

As reported in NPR, Robert Julian-Borchak Williams was picked up by police by January 2020 and when he got to the station, he was surprised to the lack of resemblance between him and the pictures of the suspect.

The officer's response? "So I guess the computer got it wrong, too." 

Regardless, "Williams was detained for 30 hours and then released on bail until a court hearing on the case, his lawyers say."

(For more on the story, check out this video)

The story is chilling, to say the least.  The knee jerk reaction is to think of Skynet and dark AI. But is that really what's happening here?

The social unrest speaks to how the desegregation struggles of the 1960s have not totally succeeded. The challenge is that racism is systemic. Within the institutions that hold society together, the gothic systems that existed in the 1950s somehow still exist until today. Sure, it's illegal for prosecutors, judges and cops to be racist. But then how do we explain the treatment of George Floyd and Robert Williams? Is there is no overall monitoring provisioning to ensure that the desired equality is achieved? For example, good monitoring controls over a system would assess the outcomes to see if the desired outcomes are achieved. There was a case that tested this idea. In McClesky v Kemp, where the defence team provided Dr. Baldus's study that statistically proved that the African American is 4.3 times more likely to get the death penalty, the "big data" analysis was rejected and Warren McClesky was put to death by the state. (And yes it controlled for 35 non-race variables).

In other words, data analysis shows there actually is a problem. However, the courts essentially denied this reality and pretended everything is okay.

What does this have to do with Racist Tech?

It means that the systems and the data are biased. Racist Tech will naturally grow out of such systems. AI and predictive policing models that use data from the court system - also pretending everything is okay - will inevitably lead to people like Mr. Williams getting caught up in the criminal justice system. Compared to George Floyd he only had to spend 30 hours in a filthy cell. But during that time he would have no idea whether it was going to be 30 hours or 30 months, given how long it takes to exonerate the innocent.

I was once asked at a conference whether we can look forward to a future where AI takes over. My response was to point out the real issues is with the human that run the technology.  If I had to answer that question today, I would simply ask them to call Mr. William who knows that the nightmare scenario is here already.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.

Are we too confident in Artificial Intelligence? A look at AI's "stupid problem"

In the rise of AI over the past few decades, the victory of IBM's Big Blue over Garry Kasparov is the stuff of legend. However, what may not be as well known is how the actual artificial intelligence alone didn't result in machine defeating man.

Many chalked up the win to Big Blue being superior technology that allowed the computer to defeat the chess champion. He narrowed down his defeat to a move that "was too sophisticated for a computer". 

What actually happened? 

It turns out that the specific move that Kasparov attributed the win too was executed by the computer. However, the chess move didn't come from the AI programming specific. It rather was more attributable to "technology controls" that there programmed into the system. The system was designed to conduct a random legal move if the system started going into an endless loop (go to 6:35 in this video to see the full story):

As noted in the video, the move through off Kasparov as the move made no sense. In the video, it insinuates that Kasparov put too much faith in the machine. However, according to Wired, he attributed the win to some type of human intervention. Either way the computer threw-off the chess championing; a factor that arguably contributed to his loss.

Enter Janelle Shane.

She did a Ted Talk entitled "The danger of AI is weirder than you think". In the talk, she notes how she applied machine learning to discover new ice-cream flavours based on 1,600 pre-existing flavours. The result:

Anyone want to hire this machine as their next culinary expert? Probably, not.

The example is illustrative of AI's "stupid problem".

As Shane notes, AI "has the approximate computing power of an earthworm, or maybe at most a single honeybee, and actually, probably maybe less. Like, we're constantly learning new things about brains that make it clear how much our AIs don't measure up to real brains."

The challenge with programming nuance into algorithms and AI more broadly speak to the classic accounting problem that goes with architecting proper incentives. We should not forget that the underlying equations that are built into such incentives are algorithms in their own right. For example, strictly profit-oriented incentives have incentivized management to look at short-term and forgo the long-run. This, in turn, resulted in more comprehensive incentive structures such as Kaplan's balanced-score-card. Putting the two together, if we programmed an algorithm to increase "shareholder value", what would happen? Would it launder money for drug cartels (i.e. because the benefit of the revenues outweighed the cost of the fine), clear-cut Amazon rainforests or outsource manufacturing to take advantage of low-cost labour in China, Bangladesh and elsewhere? As the links suggest, these are all practices that would be programmed into the sharehoder-maximizing algorithm. 

With this in mind, it requires risk and control specialists to approach AI like any system. They are ultimately programmed by regular human beings who make mistakes. If a system can throw off a reigning chess champion due to a coding flaw, we need to take heed.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

If Artificial Intelligence can identify Shakespeare's linguistic signature, can similar techniques be used in audit?

Can AI help us identify who the real authors of classic literature?

According to MIT, the answer is yes. In a recent, article they noted how machine learning was used to identify how much a co-author helped fill in the banks for Shakespeare's Henry VIII. They had long suspected that John Fletcher was the individual but couldn't identify what passages he wrote into the play.

Petr Plecháč at the Czech Academy of Sciences in Prague trained the algorithms using plays that Fletcher that corresponded with the time that play was written because "because an author’s literary style can change throughout his or her lifetime, it is important to ensure that all works have the same style".

Based on his analysis, it appears half the play is written by Fletcher.

The experiment is a proof-of-concept that there is a certain linguistic signature to how people author things. In a sense, it means we have a unique pattern when it comes to how we construct sentences. With respect to the experiment run by Dr. Plecháč, the algorithm was able to detect what was written Fletcher because he "often writes ye instead of you, and ’em instead of them. He also tended to add the word sir or still or next to a standard pentameter line to create an extra sixth syllable."

Can this be used within an audit? 

A paper co-authored by Dr. Kevin Moffitt of Rutgers University entitled "Identification of Fraudulent Financial Statements Using Linguistic Credibility Analysis" found just that. In the paper, they explained how they used a "decision support system called Agent99 Analyzer" to  "test for linguistic differences between fraudulent and non-fraudulent MD&As". The decision support system was configured to identify linguistic cues that are used by "deceivers". The papers cites as examples of how deceivers when they speak "display elevated uncertainty, share fewer details, provide more spatio-temporal details, and use less diverse and less complex language than truthtellers".

The result?

The algorithm had "modest success in classification results demonstrates that linguistic models of deception are potentially useful in discriminating deception and managerial fraud in financial statements".

Results like these are a good indication of how the audit profession can move beyond the traditional audit procedures.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

Gartner Hype Cycle 2019: What trends should CPAs care about?

Less than a month ago, Gartner released it's latest Hype Cycle for 2019.

But for we get to that, what is the Hype Cycle?

If you have heard terms like "peak of inflated expectations" or "trough of disillusionment" - you're already familiar with it. But if not check it out here on Gartner's site. As described in the link, it looks at technology going through a "bubblistic" growth curve, dividing the ascent of an innovation or technology into the following phases:





The Hype Cycle essentially captures the "herd mentality" that causes Bubbles to form in the Capitalist economic system. Efrim Boritz and I wrote a paper, "A Brief Review of Investment Bubbles throughout History", over a decade ago that analyzes the history of Bubbles going back to Tulipmania back in 1600s to the DotCom Bubble in 2000. In the paper we reference, John Cassidy's "Dot.con: The Greatest Story Ever Sold", as follows:

"According to Cassidy (2002) all speculative bubbles go through four stages: 1) displacement, when something changes people’s expectations about the future; 2) boom, when prices rise sharply and skepticism gives way to greed; 3) euphoria, when people realize the bubble can’t last but they want to cash in on it before it bursts; and 4) bust, when prices plummet and speculators incur great losses. "

This illustrates that it's not just Gartner that has understood this phenomenon, but it is something broadly understood about the maturity of a given technology within the context of a Capitalist economy.

Why should CPAs care about the Hype Cycle? 

CPAs working for companies that approve investments or provide strategic advice to business leaders will want to understand where a technology trend is before approving an investment in that solution. For example, a company who is not threatened by competition or other trends may want to wait till the technology hits the Slope of Enlightenment or later. For example, chiropractors and other health care professionals can benefit from calender and website building/hosting services that are commercially available instead of having to develop their own. Conversely, someone in a highly competitive space may want to get in much earlier to head-off the competition. For example, Blackberry was too late when it came to having an app ecosystem to compete with Andriod or iOS.

Delving into the 2019 Trends: What's new in AI & Analytics? 

In terms of the overall 2019 Hype Cycle, we see that AI is still hasn't surpassed the Peak of Inflated Expectations. This illustrates that the technology is in a Hype mode; meaning a lot has to be proven out before the full ROI of AI can be understood



In the article, the following 5 trends were highlighted:

Sensing and mobility

Augmented human

Postclassical compute and comms

Digital ecosystems

Advanced AI and analytics

In terms of the trend that arguably has the most relevance to CPAs is advanced AI and analytics.

In a supplementary link, Gartner highlights the importance of data literacy equating to speaking the same language in the organization:

"Imagine an organization where the marketing department speaks French, the product designers speak German, the analytics team speaks Spanish and no one speaks a second language. Even if the organization was designed with digital in mind, communicating business value and why specific technologies matter would be impossible.

That’s essentially how a data-driven business functions when there is no data literacy. If no one outside the department understands what is being said, it doesn’t matter if data and analytics offers immense business value and is a required component of digital business."

They go on to state that there is an essentially a looming crisis with respect to this stating that by "2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value". 

This trend is important for two reasons. 

Firstly, one of the continuing challenges for CPAs in the world of audit is to get their arms around the data. So such a stat testifies to the continuing challenge that auditors will face when designing and executing audit data analytics (ADAs). 

Second, the decision of CPA Canada to go with Data Governance as a strategic area seems to be a good choice given this trend being highlighted by Gartner.

Such trends highlights the need for CPAs to be proficient in data wrangling, extract/transact/load (ETL) and analytics. As the Gartner article notes:

"Poor data literacy is ranked as the second-biggest internal roadblock to the success of the office of the chief data officer, according to the Gartner Annual Chief Data Officer Survey. Gartner expects that, by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies."

It won't be easy to prove value with data governance because of the indirect link to cost savings or revenue enhancement. But when reputable analyst firms, such as Gartner, identify this as an important trend it makes it easier to convince the business that such investments are warranted. 

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.

Can Blockchain really offer a way out of the Brexit quagmire?

For those following the continuing Brexit crisis in the UK, there have been many issues not least of which is solving the "Irish border" issue. If you need more context on this issue, see the following video by Vox Atlas which does an amazing job of summarizing the issues in about 7 minutes:


What does this have to Blockchain?

Well, it seems that blockchain was identified as a possible solution for this situation. I came across this idea from an article in CCN, which stated the following:

"According to Phillip Hammond, UK’s finance minister, the best way to ensure trade across the Irish border remains frictionless after Britain leaves the EU lies in the use of blockchain technology.

“There is technology becoming available (…) I don’t claim to be an expert on it but the most obvious technology is blockchain,” Reuters reported Hammond as having answered after being asked what the government was proposing to do to ensure smooth trade after Brexit."

I followed the Reuters link but it didn't add much context to the quote; how can blockchain offer any relief from the issues related to the customs union and hard border?

But then I found an article on FT, which stated the following:

"It is safe to say technology used at the border is a red herring, as even the best database can't poke its nose inside a lorry. Here, for instance, is one of the IT experts quoted in the Irish Times calling the idea of technological solutions to the border question “complete nonsense”...

Wired also looked at tech solutions for dealing with 6,000 heavy goods vehicles per day crossing the border, and decided that they were “untested or imaginary”. Blockchain as a border solution is both.

So what inspired Hammond to jump on the blockwagon? It might have been a “white paper” literally called “Blockchain for Brexit”, released last week by Reply Ltd, a consultancy which promised a “solution that could save global businesses billions of pounds through seamless border checks and virtually infallible tracking systems for their goods”."

Although I have commented that blockchainthusiasts need to be careful about overstating the capabilities of the blockchain (such as replacing the need for financial audits), we can hardly blame blockchainthusiasm here. Rather it's the Wizard-of-Oz trick of hiding behind the magic curtain. But this time it's not a magic trick but rather the complexity of technology that some are attempting use to gloss some key issues that have emerged in the aftermath of Brexit.

Technology at the end of the day is just a tool fashioned by human beings and is not God. It can't magically solve complex business problems let alone extremely complex political issues that have been simmering for centuries.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

What's missing from this Top 5 uses of Blockchain list?

TechRepublic's Tom Merritt  walks us through the "Top 5" uses of blockchain in the following video. The accompanying post lists the following 5 use cases:

• Stocks
• Shipping
• Diamonds 
• Livestock 
• Law

What's missing? 

Stocks use case is actually limited to Initial Coin Offerings (ICOs). For more on an overview of ICOs, check this article. However, the post excluded Linq's blockchain that allows for the settlement of private securities.

But on a broader note, the post excluded the financial industry altogether in terms of being a forerunner for the use of blockchain. Following the hype-cycle, one of the early areas of interest for the use of the permissioned blockchain were financial institutions. It seemed like every week that a company joining the R3 Consortium.


However, since that initial fervor, a number of players, such as Goldman Sachs, Santander, Morgan Stanley and the National Australian Bank, have left the consortium.

Why?

The problem lies in understanding the actual business case for the permissioned blockchain (for the differences between public and private/permissioned, see this post). The permissioned blockchain helps parties to have a common view of transactions that they have transacted with each other via a shared ledger database. With the use of digital signatures, it incorporates authorization into this as well, so in addition to sharing information, it also enables the ability to "sign-off" on that information.

The banks could decide that they would use such a framework to make it easier to settle payments, however, how do they keep things private such as pricing and other data? This is something that needs to be sorted out but points to a bigger question as to what is the strategic advantage of blockchain for FIs. That is, this exponential technology doesn't lead to cost savings like robotic process automation or strategic insights like big data analysis.

And that's why I think something like shipping or supply chain more broadly is a much better beachhead for blockchain. With multiple partners involved in supply chain, have a shared database enables the partners to see where things are at between the wholesaler, shipper, and retailer, enabling each partner to get better insights into movement of goods and other business information. Such a system would allow for creative ways to settle payments or even enhance the ability of retailers to design consignment contracts with wholesalers. For example, BestBuy is marketplace (e.g. Brainydeal is one such retailer) within its retail front requiring such coordination. The one caveat, however, is to ensure that (cheaper) existing technology doesn't actually do this already. After all, shared databases are not a novel concept.

I would contend that legal would be a great place for the blockchain to expedite paperwork - more so than supply chain. However, such technology would be fought tooth and nail by lawyers. And they have unlimited resources to fight such technology in the courts. Also, politicians have little incentive to look into such advances as most of them are lawyers, depend on lawyers or have friends who are.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.

What can driving algorithms tell us about robo-auditors?

On a recent trip to the US, decided to opt for a vehicle with the sat-nav as I was going to need directions and wanted to save on the roaming charges. I normally rely on Google Maps for guiding me around traffic jams but thought that the sat-nav would be a good substitute.

Unfortunately, it took me on a wild goose chase more than once – to avoid the traffic. I had blindly followed the algorithm's suggestions assuming it would save me time. I ended up being stuck at traffic lights waiting to a left-turn for what seemed like forever.

Then I realized that I was missing was that feature in Google Maps that tells you how much time you will save by taking the path less traveled. If it only saves me a few minutes, I normally stick to the highway as there are no traffic lights and things may clear-up. Effectively, what Google does is that it gives a way to supervise it’s algorithmic decision-making process.


How does this help with understanding the future of robot auditors?

Algorithms, and AI robots more broadly, need to give sufficient data to judge whether the algorithm is driving in the right direction. Professional auditing standards currently require supervision of junior staff – but the analogy can be applied to AI-powered audit-bots. For example, let’s say there is an AI auditor assessing the effectiveness of access controls and it’s suggesting to not rely on the control. The supervisory data needs to give enough context to assess what the consequences of taking such a decision and the alternative. This could include:

• Were controls relied on in previous years? This would give some context as to whether this recommendation is in-line with prior experience.
• What are the results of other security controls? This would give an understanding whether this is actually an anomaly or part of the same pattern of an overall bad control environment.
• How close is it between the reliance and non-reliance decision? Perhaps this is more relevant in the opposite situation where the system is saying to rely on controls when it has found weaknesses. However, either way the auditor should understand how close it is to make the opposite judgment.
• What is the impact on substantive test procedures? If access controls are not relied on, the impact on substantive procedures needs to be understood.
• What alternative procedures that can be relied on? Although in this scenario the algo is telling us the control is reliable, in a scenario where it would recommend not relying on such a control.

What UI does the auditor need to run algorithmic audit?

On a broader note, what is the user interface (UI) to capture this judgment and enable such supervision?

Visualization (e.g. the vehicle moving on the map), mobile technology, satellite navigation and other technologies are assembled to guide the driver. Similarly, auditors need a way to pull together the not just the data necessary to answer the questions above but also a way to understand what risks within the audit require greater attention. This will help the auditor understand where the audit resources need to be allocated from nature, extent and timing perspective.

We all feel a sense of panic when reading the latest study that predict the pending robot-apocalypse in the job market. The reality is that even driving algos need supervision and cannot wholly be trusted on their own. Consequently, when it comes to applying algorithms and AI to audits, it’s going to take some serious effort to define the map that enables such automation let alone building that automation itself.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

CPAOne: AI, Analytics and Beyond

Attended the CPA One Conference almost two weeks ago in Ottawa, Ontario. Given that my space is in audit innovation, I attended the more techno-oriented presentations. Here's a summary of the sessions that I attended:

"Big data: Realizing benefits in the age of machine learning and artificial intelligence": The session was kicked off by Oracle's Maria Pollieri. The session delved deep in the detail of machine learning and would have been beneficial to those who were trying to wrap things around thing more from a technical side. She was followed up by Roger's Jane Skoblo. She mentioned a fact that really grabbed my attention: when a business can just increase its accessibility to data by 10%; it can result in up to $65 million increase in benefits.

The next day started with Pete's and Neeraj's session on audit automation, "Why nobody loves the audit". They want over a survey of auditors and clients on the key pain points of the external audit. It turns out that these challenges are actually shared by both. For example, clients lack context on "the why" things are being collected, while auditors found it difficult to work with clients who lacked such context. On the data side, clients have hard time gathering docs and data, while the auditors spent too much time gathering this information. From a solutions perspective, the presenters discussed how Auvenir puts a process around gathering the data and enables better communication. This will be explored in future posts when we look at process standardization as a key pre-requisite to getting AI into the audit. 

The keynote on this day was delivered by Deloitte Digital's Shawn Kanungo, "The 0 to 100 effect". The session was well-received as he discussed the different aspects of exponential change and its impact on the profession (which was discussed previously here). One of the key takeaways I had from his presentation was how a lot of innovation is recombining ideas that already exist. Check this video he posted that highlights some of the points from his talk:



Also, checked out the presentation by Kevin Kolliniatis from KPMG and Chris Dulny from PwC, "AI and the evolution of the audit". Chris did a good job breaking down AI and made it digestible for the crowd. Kevin highlighted Mindbridge.ai in his presentation noting the link that AI is key for identifying unusual patterns.

That being said, the continuing challenge is how do we get data out of the systems in manner that's reliable (e.g. it's the right data, for the right period, etc.) and is understood (e.g. we don't have to go back and forth with the client to understand what they sent).

Last but not least was "Future of finance in a digital world" with Grant Abrams and Tahanie Thabet from Deloitte. They broke down how digital technologies are reshaping the way the finance department. As I've expressed here, one of the keys is to appreciate the difference between AI and Robotic Process Automation (RPA). So I thought it was really beneficial that they actually showed how such automation can assist with moving data from invoices into the system (the demo was slightly different than the one that can be seen below, but illustrates the potential of RPA). They didn't get into a lot of detail on blockchain but mentioned it is relevant to the space (apparently they have someone in the group that specifically tackles these types of conversations).


Kudos to CPA Canada for tackling these leading-edge topics! Most of these sessions were well attended and people asked questions wanting to know more. It's through these types of open forums that CPAs can learn to embrace the change that we all know is coming.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

Serendipity: Beyond the reach of Robot Professionals?

Came across a story about how Dr. Behfar Ehdaie at Memorial Sloan Kettering Cancer Center was figuring out how to deal with the emotions that come with a discovery of prostate cancer. His novel solution gives may give us some insights into the limits of robots in the professional world.

What he found was that his patients opted for radical treatments, such as surgery or chemotherapy, that resulted in side-effects that actually ended up being more harmful. To use a cliche, the cure was worse than the cancer.

For such patients, "the medical consensus is that active surveillance often is the appropriate treatment for small early tumors". Of course, such an approach is not risk-free, but the problem is that "despite the data showing that this approach is safe, about 50% of eligible men don’t get it either because they turn it down or their physicians don’t embrace it. Medical experts say many men have been overtreated, as their cancers probably posed little immediate danger."

What was his solution?

Negotiate with patients.

As noted in the WSJ article referenced above, he contacted Harvard professor Deepak Malhotra who had authored an article on the topic to develop strategies on negotiating with the patient. Leveraging lessons from behavioral economics was to make monitoring the anchor instead of surgery or chemo. Dr. Ehdaie and professor Malhotra devised a lecture that was delivered to doctors to help them learn from Dr. Ehdaie's successes with this approach.

But what does this have to do with limits of robot-professionals or robopros?

When it comes to cancer treatment and robots, one can't complete the conversation without mentioning IBM's Watson "Oncology Edition". In fact, IBM has a partnership with the same Memorial Sloan Kettering Cancer Center that Dr. Ehdaie works at. Here is a promo-video that speaks to the promise of Watson:

The key to understanding the limits to robot-professionals is the backstory on how Dr. Ehdaie first decided to explore negotiations a way to deal with the issue.

This is where serendipity comes to play.

He was exposed to such concepts with discussions with his wife who is an MBA. Meaning that he went beyond the cancer treatment journals and then discovered a non-standard approach to dealing with a problem. Robots are not good at this. Machine-learning and AI is only good as what you teach it. Even "simple" tasks require thousands of man-hours to train such algos. Perhaps this can be overcome, but currently, it is a real limitation of AI.

Does this make humans indispensable?

Really depends on the objectives that govern the profession and the organizations that hire them.

If it's about cost-cutting and making the process efficient and streamlined, robots are perfect creating a fossilized bureaucracy that is resistant to change. Think about how financial institutions have yet to overhaul their ancient banking systems coded in COBOL:

"In the United States, the financial sector, major corporations, and parts of the federal government still largely rely on it because it underpins powerful systems that were built in the 70s or 80s and never fully replaced."

Similarly, if Dr. Watson replaces a large component of the diagnostic process it would become hard to dislodge it from the cancer treatment process.

On the other hand, if organizations recognize the value of human beings in being important to overall objectives of the profession - patient care, audit quality, etc. - then human judgment must be hardwired into the organization's DNA to avoid the development of such an inflexible system.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

Did artificial intelligence kill the BlackBerry? It did for me.

Recently, Globe and Mail noted in their political briefing newsletter that Samsung's Knox software is deemed to be as secure as the traditional BlackBerry:

"Shared Services Canada, the department in charge of overseeing IT for the federal government, is set to offer alternatives to bureaucrats over the next 18 months as part of “a new approach to mobile service to better serve its clients, use new technology and adapt to changes in the marketplace.” Samsung and its line of Android-powered smartphones was the first to be approved by Shared Services, but only after two years and several tests showed that Samsung’s phones passed military-grade requirements."

In this blog, we've covered Blackberry's steady fall into oblivion. For me, BlackBerry was my first smartphone. I even got excited about the Torch, thinking it was the perfect compromise between the touch screen and the classic keyboard. However, that feeling faded quite quickly after using the device. It was so underpowered and underwhelming compared to the competition.

When looking at the Porter 5 forces that surrounded this once mighty Canadian tech giant, we could say that both Apple's iOS and Android offer better substitute's: better devices, more power, better apps, etc. Essentially, these devices has evolved so much that they bring the power of the PC into the palm of one's head (Samsung's Note 8 is expected to have 6GB of RAM!).   This alone doesn't explain why BlackBerry was ultimately displaced from corporate IT - well before Samsung's Knox became equal-to-BlackBerry in terms of security.

I think there were two key developments that enabled BlackBerry's decline.

The more well known one is the "consumerization of IT" phenomenon: users wanted to use their latest iPhone or Android device instead of the BlackBerry in the corporate environment. Going back to Porter 5 forces this speaks to "bargaining power of buyers": the people no longer wanted to be limited to the "one trick pony" of email and BB Messenger. And they were willing to lobby their corporate IT departments to bring on the Android and Apple devices.

This leads to the second less well known factor.

What allowed consumerization to take place was that Microsoft took an open approach to licensing it Exchange Active Sync. This move paved the way for iPhone and Android to connect their devices to the corporate email server. Microsoft open attitude essentially transferred the power from BlackBerry to the consumer.

But for me it was a little different. Of course I like the apps and GPS that my Android and iPhone bring to me: the ability to read the Kindle, listen to audio books and podcasts without having to carry multiple devices is definitely a productivity. But really it was one particular app that made me able to switch: SwiftKey.

And that's where we get to the artificial intelligence.

I was a big fan of the Blackberry,  primarily because I thought I couldn't live without the physical QWERTY keyboard. But friends who were encouraging me to switch mentioned that Android sported the SwiftKey keyboard which is powered by artificial intelligence. This keyboard is much better for me in terms of learning the words I use when I type than the predictive text feature in its iOS counterpart (which I use for work).


A little while ago, I tried a colleague's BlackBerry and the irony is that my hands hurt. And that's probably because I type a lot less specifically because of the AI approach taken by SwiftKey. As per the native analytics tracker (see graphic below) in my app I have been saved myself over 350,000 taps and am 28% more efficient. 

Today, over a quarter billion people use SwiftKey on their mobile devices. Although a little hidden from the view of analysts and academics, advances in artificial intelligence enabled SwiftKey (now owned by Microsoft) to offer a substitute for the once dominant BlackBerry physical keyboard. And for me personally it was this little piece of exponential technology (along with the relatively giant landscape of the Samsung Note 1) that convinced me it was time to switch.

And like I said, I can never go back.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

Cafe X and Amazon Go: Auditing a robot-operated store?

By now you've probably heard of the robot-barista - Cafe X.  If not check out this video from Wired, where David Pierce walks us not only through how the robot will make your latte, but why he thinks it better than the human alternative:



Amazing isn't it?

In a presentation I did last year on how these forces of automation could impact auditing & accounting, I noted it's easier to see how technology disrupts someone other than you.

And so it looks like baristas have met their match.

As Pierce notes in the video, the inconvenience of dealing with imperfect people is something that most people want to avoid in the rat-race we live in: who wants the barista to remake your coffee 11 times as he says? ;) 

The Wired article also notes that Cafe X is 'high-quality at a cheaper price': 

"Surprisingly delicious coffee, starting at $2.25—cheaper than you’d find at Sightglass or even Starbucks. Cafe X’s location in the corner of the Metreon may not entice you out of your daily routine."

Amazon Go: Walkthrough Technology 
Amazon has also wowed the "techthusiasts" out there with their cashier-less store concept:



In the FAQ section, Amazon summarizes how this cashier-less store works:

"Our checkout-free shopping experience is made possible by the same types of technologies used in self-driving cars: computer vision, sensor fusion, and deep learning. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt."

Although this has the potential to revolutionize retail, Amazon has experienced some setbacks of late. The store can allegedly only handle 20 people at a time. So there maybe some kinks to work out before this goes mainstream.

Obviously, this could have a massive impact on entry level jobs: most of us who were young a while ago relied on these McJobs for spending money and funding our college/university tuition. They also gave students some practical work experience to help land a career accounting profession ;)

But let's save this discussion for a future post.

How would you audit cashier-less stores, like Cafe X or Amazon Go?

The retail industry has been a manual intensive industry that requires cashiers, stock room personnel and the like. Such a process naturally requires policies and procedures (aka internal controls) that ensure that merchandise makes it from the shelf to the cash register and into the customers possession. And there are those anti-theft mechanisms to prevent shoplifting as well. In the industry, "shrinkage", the amount of merchandise that is stolen, robbed, damaged, etc, is estimated by the National Retail Federation to be 1.38% of sales or $45.2 billion for 2015.

Cafe X and Amazon Go offer a glimpse into how automating traditional businesses can alter these fundamental risks that impact the way we go about conducting our financial audits.

With Cafe X, shrinkage is almost eliminated as there is no humans involved in the production process. Once the kiosk is loaded up with cups, coffee, syrup, sugar, milk, etc. the system is essentially fully automated - no manual intervention by baristas or customers.

Amazon Go, on the other hand, uses a whole lot of automation that is watching and analyze every move of the customers (and employees) throughout the store. Consequently, this would not be the store to steal from! And let's not forget Amazon is experimenting with those drones and are we really sure that they are unarmed?


Given this level of automation of the actual business process and controls, could auditors stick to the tried, tested and true retail audit procedures? Or would this enable a more automated approach?

I was directly involved with the recent test-audit of the blockchain involving loyalty points. One of the realities of auditing such exponential technologies is that it makes controls testing a must. For example, for the financial auditor to rely on the digital signatures there needs to be some testing around the wallets to ensure that the signatures are reliable.

Consequently, testing such automated stores would require either a SOC2 or modified SOC report to meet the needs of such a store. For example, the SOC2 would need to have some way of having comfort of how the stock and inventory gets loaded into the store. Likely the auditor would rely on the automated process which the store uses to replenish stock, but it's that hand off between the delivery person (assuming it's still human) that would be the area there is a risk of shrinkage. For example, how does legitimately damaged inventory get accounted for at that point? Whatever process and controls Amazon/Cafe X put in place would need to be tested from a controls perspective.

For the substantive component, I think that's where things get interesting: enter the "embedded audit module". This concept has been around since at least 1989. The idea is that the auditor installs independent software onto the client's system and then transmits it back to the auditor, who uses it as a basis for conducting the necessary audit procedures and tests. The core idea is that the auditor has full control over such a system and the client cannot tamper with the code.

What would be relatively straightforward would be the data capture-component: sales data, stock data, spoilage, etc. would be uploaded from the automated store right into the auditor's system. But this then requires the additional step of verifying the data to independent source documents (e.g. invoices, purchase orders, etc.). In other words, the audit procedure would still require manual intervention as the auditee would need to send this information back to the auditor to complete their audit.

Where I think the audit innovation would be is exploring how video footage can act as a substitute for physical/direct observation by the auditor. That is, could the auditor install a video camera in the automated store as a part of the EAM that would then act as actual independent audit evidence of the actual sale or purchase? For example, in the Cafe X example the auditor could actually use the footage and the visual software to count the cups sold that day and reconcile that to the sales data transmitted back from the EAM for the day?

Although one can argue such transactions are not material and therefore such procedures are overkill.

However, I think now is the right time to conduct experiments and test audits to see whether we can reinvent the classic audit to meet the technology of today. In a future post, we will explore what this means broadly for jobs and more specifically how this could impact the profession.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

RPA and the Accountant: A path out of the mundane?

One of the latest hype technologies is Robot Process Automation (RPA).

My first question when coming across this, is what is the difference between this and cognitive computing? 

As can be seen by these videos, it's more about "dumb" automation instead of "smart" innovation: where routine tasks are handled by the system instead of a person. This is in contrast to something like IBM's Watson, which attempts to understand language and offer probabilistic judgments as to what is the right answer to a question like it did on Jeopardy!


The first video (produced by Deloitte UK) does a great job of actually showing us how RPA can automate the process of extracting information/documents from email and the generating invoices through the company's ERP:

The strength of this video (produced by EY) is showing us the business case for RPA:

The idea is that RPA can automate routine tasks, instead of offshoring. In other words, it brings the world of automation onced reserved for the assembly line to the back office.

As described in this Deloitte publication, it puts RPA as the first step towards a cognitive enterprise - automate the task and then bring cognitive, AI, machine learning, etc., into the process to make it smarter.

To use a maturity model approach, RPA is the first level in bringing together the necessary data and processes to actually train the algorithm to make it smarter.

What does this mean for auditors and accountants?

For accountants, the back office is going to require less people in terms of executing these mundane tasks.

However, this doesn't necessarily mean that jobs will be lost.

As with the advent of cloud computing, the enterprises will have to determine whether such talent can be used more effectively to improve the quality of financial reporting and work on the back log of finance projects that haven't been attended due to staff working on these low-value tasks. That being said, the problem of meeting quarterly targets to feed investors insatiable desire for profits is something that can't be ignored when discussing whether management will choose profits over better processes.

For auditors the story is a little different.

The reality of the profession is that it can't retain talent because people find the work unsustainable: it's hard to shutdown your personal life for a third of the year or more to meet the needs of clients during busy season.

RPA and automation could make the profession more sustainable, as these mundane tasks could be handed to a system instead of a junior. This is similar to the "race with the machine" concept I mentioned in this post, when referring how Watson is helping doctors treat cancer.  Auditor could then focus on more value added tasks, such as assessing aggregate risks, industry trends, etc. Such insights will improve audit quality and give clients better understanding of business and audit risks, making the work more interesting for both auditors and auditees alike.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else.

Virtual Personal Assistants: How far will they go? Part 1

Gartner in a recent press release gave some predictions around "virtual personal assistants".

What are virtual personal assistants or VPAs?

Currently, they are the not-so-perfect voice-activated software that accompanies our mobile devices - Apple has Siri, Microsoft has Cortana and Google has Google Now

On the latest Google phone, Pixel, they have Google Assistant:


Although only available for limited release, the video is actually a good summary of the promise of VPAs: the software that will help us coordinate our lives through our-ever-so-central-to-our-lives smartphones.

And that takes us back to how important these VPAs will become. According to Gartner, within two years 20% of all interactions with our smartphones will be through VPAs.

The press release from the research giant also noted some interesting stats on how frequently people are using Siri and Google Now.

In the UK/US, 54% of people surveyed used Siri in the last 3 months. With respect to Google Now, 41% have used it in the UK and 48% have used the service in the US (in the last 3 months). They also noted that they will move from simple tasks (e.g. setting alarms) to more complicated things such as executing transactions.

By 2020, Gartner predicts that VPAs combined with machine learning, IoT, biometrics and other technologies will enable 2 billion devices to operate without a touch interface.

How far can this go?

When I was thinking about writing this post, I thought about my first interaction with an artificial intelligent assistant.  However, before going there I thought it would be first interesting to go back to the movie "Her".

I saw the movie on the plane on one of the business trips that I took.

The movie is about the ultimate stage of, well, virtual personal assistants.

As noted in the trailer below, the "OS" is something that exists on the mobile device but acts as a central management point that brings a persons data together. In the movie, the OS (voiced by Scarlett Johansson) has a real personality that in a sense accompanies the protagonist, played by Joaquin Phoenix, everywhere. The movie goes a bit crazy as they apparently start "dating".

On a side note, I thought the movie was interesting as it speaks to how technology has filled the void in the life of the atomized individual. The story shows how the protagonist has had a bad breakup and turns to this OS for substitute companionship.

Sure this is far-fetched.

But how many times have we left a real conversation with a real loved one only to get to the virtual world of our phones? Of course, it's not some fake person but it's not difficult to see how we could switch the artificial world of VPAs because we have become accustomed to interacting with this endless streams of notifications.

The other part of the movie that I found interesting was how the mobile device is so nondescript. For someone like myself, smartphones have always had this novelty. But in the movie it's a not anything exciting to look at it. In a sense, what's more important is the actual OS running the device. As Gartner predicts, what becomes more important is the "touch-free interaction" between the OS and Joaquin - and the device disappears into the background.

Only time will tell how far this technology go. But I think it's fairly easy to see how such VPAs will become more entrenched in our lives the more "human" they become.

Big Data and Predictive Policing: Can algorithms become racists?

Interesting article on Forbes by Thomas Davenport on Big Data. The articles discusses how various government, including Canadian Public Safety Operations Organization (CanOps), have used big data tools for "situational awareness". These systems draw on myriad sources of data to give users (e.g. law enforcement) the information they need to deal with a particular situation.

Here are a few points that I thought were worth noting:

Government is making strides in big data: We often think of Amazon, Google and other tech-giants as key users of this data. However, as the Davenport points out that the government is using this technology to assist with decision making. However, whether this is something that should be celebrated remains to be seen (see predictive policing below)

Privacy versus Value trade-off: He talks about how CanOps use of MASAS, the Multi-Agency Situational Awareness System, is limited by the filtering of sensitive information: "breadth of MASAS is noble, but it seems to limit its value. For example, as the CanOps website notes, because agencies are reticent to share sensitive information with other agencies, all the information shared was non-sensitive (i.e. not terribly useful)." It seems that this continues to be a theme that we had noted in back a couple years when discussing a similar trade-off the companies face when dealing with big data. As I noted in this post:

"privacy policies require the user to consent to a specific uses of data at the time they sign up for the service. This means future big data analytics are essentially limited by what uses the user agreed upon sign-up. However, corporations in their drive to maximize profits will ultimately make privacy policies so loose (i.e. to cover secondary uses) that the user essentially has to give up all their privacy in order to use the service."

Consequently, there still needs to be a solution as to how privacy can be respected but organizations can use the data they have collected to make better decisions.

Predictive Policing is an emerging reality: The sci-fi movie, Minority Report, paints a future where law enforcement arrests people before they commit crimes.


That future seems to be well on its.  Davenport mentions how "predictive policing" was introduced in 2014 to the NYPD.  He also mentions how much data is being collected by the police:

"It collects and analyzes data from sensors—including 9,000 closed circuit TV cameras, 500 license plate readers with over 2 billion plate reads, 600 fixed and mobile radiation and chemical sensors, and a network of ShotSpotter audio gunshot detectors covering 24 square miles—as well as 54 million 911 calls from citizens. The system also can draw from NYPD crime records, including 100 million summonses."

The idea of predictive policing was also raised in the book,  Big Data: A Revolution That Will Transform How We Live, Work, and Think, which I had explored in a multi-blog post series (click here for the first installment).

Andrew Guthrie Ferguson, Law professor UDC David A. Clarke School of Law, wrote an article on how that predictive policing is something that has not be really sorted in out in terms of legality. He notes:

"The open question is whether this big-data information combined with predictive technologies will create “predictive reasonable suspicion“ undermining Fourth Amendment protections in ways quite similar to the stop-and-frisk practices challenged in federal court.

In two law review articles I have detailed the distorting effects of predictive policing and big data on the Fourth Amendment and have come to the conclusion that insufficient attention has been given at the front end to these constitutional questions. New York has the chance now to address these issues before the adoption of the technology and should be encouraged by the same civil libertarians and ordinary citizens who challenged the stop and frisk policies."

His commentary highlights another limitation: big data predictions are biased based on how the data is collected. The stop and frisk policies he refers to disproportionately targeted minorities. Furthermore, policing is more focused on poor, black/hispanic neighbourhoods. Michelle Alexander documents in her book, The New Jim Crow, how this happens:

"Alexander explains how the criminal justice system functions as a new system of racial control by targeting black men through the “War on Drugs.” The Anti-Drug Abuse Act of 1986, for example, included far more severe punishment for distribution of crack (associated with blacks) than powder cocaine (associated with whites). Civil penalties, such as not being able to live in public housing and not being able to get student loans, have been added to the already harsh prison sentences."

Consequently, if the data by law enforcement is used to predict crime that essentially the targeting of minorities will continue to target such groups given that it is based on biased data. 

Technology often is seen to be a silver bullet for problems. However, we need to keep in mind that it is vulnerable to the human element that makes it. Given Microsoft's recent faux pas of accidentally allowing an AI avatar to become a Nazi, it is something that should actively be considered in the systems that are built to police and govern. 

Artificial Intelligence: The new "space race" for the tech-giants?

When IBM's Watson defeated Ken Jennings and Brad Ritter on Jeopardy!, it was a shock. As Ken Jennings describes in this Ted Talk, he had no idea that a computer could possibly defeat him at Jeopardy! On this Ted Talk, Ken Jennings describes how he never thought that a computer could beat him:



And he's right.

How can a computer possibly understand that "feel can smell" and a "nose can run"? 

But on February 16th 2011, IBM's Watson did precisely that: it was able to defeat the two reigning human champions Ken Jennings and Brad Rutter. And with that IBM ignited the space race for artificial intelligence.

Although people may point to the wide array of personal digital assistants from Apple (Siri), Microsoft (Cortana) or Google Now as the true birth of the AI space race. However, these application are limited to the use of the personal arena. Anyone who used things like Google Now - which can link your calendar to traffic patterns and tell you if you'll be late for appointment - can tell how amazing it is to how have a digital assistant work behind the scenes to keep your day on track. That, however, is limited to the consumer realm. Where AI gets real interesting is the B2B realm: Watson has made some strides in automating the FAQ process. However, it's real promise has been demonstrated in the cancer treatment realm, where it enables doctors to "race with the machine" combining the millions of pages of medical journals and articles to determine the best cancer treatment for patients.  Watson is available in a cloud offering to developers who submit applications.

But IBM is not alone and so the AI Space Race is on!

As for the other vendors, see the following:

• Google's prediction API
• Amazon Machine Learning
• Microsoft's Azure Machine Learning

However, the one that I am really waiting to hear about is coming from the makers of the Siri, Viv.ai. They are hoping to build AI as a service, similar to Bluetooth, that will be embedded in all hardware. I will leave you with the following quote from the Wired article that discusses the possibilities of Viv.ai:

"Viv...generat[es] its own code on the fly, no programmers required. Take a complicated command like “Give me a flight to Dallas with a seat that Shaq could fit in.” Viv will parse the sentence and then it will perform its best trick: automatically generating a quick, efficient program to link third-party sources of information together—say, Kayak, SeatGuru, and the NBA media guide—so it can identify available flights with lots of legroom. And it can do all of this in a fraction of a second."

Windows 10: Microsoft Strategic Plays hidden in its free OS upgrade!

In a previous post, we posted the integration of Cortana into the upcoming release of Windows 10. Well, the excitement continues - Joe Belfiore walks us through a number of features:


This includes:

• Continuum: Not only is the start menu back, but the start menu adjusts for desktop mode and (touch) tablet mode. 
• Cortana: He confirms what we saw last time, but he couldn't risk a dig at Siri. But to be fair, Cortana has more of the "virtual digital assistant" features which incorporate artificial intelligence and machine learning to, as he shows, book appointments and reminders. 
• Built-in Apps: Microsoft is offering calendar, photo, maps, video, mail and xbox apps. 

Although these key features are exciting, the bigger deal is how Microsoft is working to recapture market share from its competitors. 

• Free upgrade if you have Win7, 8, or 8.1! Yes, that's right for a year people upgrade for free to W10! This obviously good news for consumers. However, it appears that businesses can also upgrade which could be the real benefit: Microsoft effectively is facilitating the move to the next version thereby reducing the risk that companies will stick with an OS for decade (i.e. like they did with XP). The free upgrade also will go a long way to build bridges with customers who were unhappy with start menu disappearing in Win 8. 
• Windows Phone and XBox integration: The apps mentioned work across devices. Although it is not clear, it appears that to get the most out of the W10 features, you need to get a W10 phone. Although this seems like a long shot, it shows that Microsoft is not giving up anytime soon on the mobile phone space. XBox integration enables Microsoft to further capture space within the living room entertainment space, competing with the likes of Roku, Apple TV and the Google Chromecast. 
• Bringing social to the web browser:  Losing market share to Google Chrome over the past few years, Microsoft appears to be striking back with Project Spartan, The browser offers enhanced usability features (tabs, reading,etc), but also has a a productivity play where users can annotate websites and then share their annotations via social apps.    

The new Windows 10 looks pretty amazing and now that it's free I really can't wait to try it!