The AI Toolkit

MAINTAIN AND SUPPORT

As previously mentioned, machine learning tools need ongoing

updates, especially in terms of data to keep the models current and

meaningful. New tools will be acquired and older tools can be retired,

although decommissioning a tool will not be as frequent for typical

software that is based on a defined set of rules or logic. AI tools are

more data driven, and the algorithm that is used to create a model is

stable and less susceptible to technology or environmental changes.

The amount and frequency of support activities will depend largely

on the strategic decisions made in acquiring the tools. One major

concern will be the location of the data being used by the AI tools and

whether the vendor or cloud provider stores a copy of the data. That

has implications for privacy and security, which is discussed in a later

chapter. Allowing vendors access to your data is dangerous because

they can still retain privacy of the data but use it to build a model

themselves that is used by a different organization.

There is a third option that is a blend of make or buy. With this

selection, the decision is about how much of a vendor’s services

should be purchased and how much work will remain within the

organization. There is a growing number of vendor-based IT

resources and services available, as well as several cloud-based

providers, that include machine learning capability as part of their

services. It is best to gain knowledge about these solutions, preferably

from someone who has experience with each one, because the

descriptive language and marketing content tend to simplify yet

overstate the actual capability. 

is available:

AWS cloud. Amazon Web Services includes machine learning

capability and pre-trained services, such as computer vision, language

processing, and forecasting. This site provides developers with the

ability to quickly build and deploy machine learning models with a

workflow that includes data labels and data preparation as well as how

to tune models to optimize them for deployment. That’s mainly a

marketing description because the work is more complex than it

sounds.

Heroku. This is a cloud-based platform for building applications and is

frequently used by start-ups for the free sandbox. It has the ability to

scale with the business and supports numerous software languages.

This is a primary selection for my researchers to host their software.

Google AutoML. Google offers a suite of tools to perform machine

learning, which allows developers with limited expertise to build and

train models. AutoML includes a labeling service as well as support for

data cleansing, which results in high-quality data for the algorithms.

Twilio Autopilot. This website allows users to quickly build chatbots

that function for a variety of online and mobile apps. Once again, it

sounds good, but the reality is more complex when attempting to build

applications for project management.

THE RISKS OF IMPLEMENTATION

Acquiring and implementing AI tools is a great opportunity, and similar

to all changes, there are risks. The first risk is security and privacy.

Will the people who have access to the model results also have

access to the training data? In some cases, this is a good thing. On

the other hand, for sensitive data, restrictions need to be considered

and any loopholes must be closed. 

infiltration. Think of this as a malicious virus intended to corrupt a

system or something like ransomware where the owner of an infected

system is asked to pay a fee to unlock databases. An attack on

training data can result in disastrous consequences, mainly with a

decision to perform the exact opposite of what a true machine learning

algorithm would normally produce with good data. I don’t know why

some people are malicious, but it happens. Let’s say a country is in

the process of launching an extremely valuable satellite into space. A

dissident group hacks into the launch system database and adds fake

data to the machine learning training datasets. The launch fails and

the dissidents expose their work so that they can take credit and gain

publicity. Organizations need to secure the data against data attacks

similar to the efforts that are already underway currently for many

organizations. The difference is that attacks on machine learning

datasets are likely to be less noticeable but they produce bad results.

A second risk is biased data, something already mentioned

elsewhere. There needs to be a commonsense assessment of

machine learning results in order to validate the outcome. There are

times when a machine learning result is accurate based on the data

but the data itself is not representative of the current environment.

Another risk is poor extrapolation of data or incorrectly interpreting

statistics. It has been suggested that a budget allocation be set aside

for auditing or validating machine learning results.

17 These issues will

be downplayed by vendors, and in the early days of AI, the

occurrences of these types of risk are low. That will change as AI

becomes more pervasive, and the biggest risk is that you have a

problem in the AI tools that goes completely unnoticed. There are

always risks with new technology, and the most reasonable response

is to become knowledgeable in how to manage or eliminate the risks

the same way that we reduce or eliminate the probability and impact in

project risks.

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