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Explaining data science, AI, ML and deep learning to management — a presentation and a script — Part 2 of 3
This series of three posts is meant to serve as an accompanying script for the Prezi presentation “Data science, AI, ML, DL and all that jazz”.
The presentation can be found here: This chapter is divided into five sections: Rather than jumping right away into a definition of AI in an abstract, academic and dry way, we would like to first review three popular applications of AI.
It perceives (gathers data about) its environment through a rich set of sensors (cameras, radars, lidars, GPS) and it is also endowed with actuators that allow it to perform actions (steering, accelerating) to change its own situation in — or otherwise affect — the environment.
Beyond sensors and actuators, which roughly correspond to the input and output layers, there is an intermediate processing layer that interprets the raw data coming from the sensors and turns it into information/knowledge that is ultimately used to solve the problem of driving safely and taking you from A to B.
To list just three, we have: Although sometimes invisible, recommendation systems are virtually everywhere, starting from e-commerce sites — think Amazon — and going all the way to digital content distribution platforms such as Netflix and Spotify, as well as social networks.
Technically, a recommendation system can be seen as an application of one of the following general AI techniques: Application 3: Computer vision We briefly touched on this application as part of our discussion of self-driving cars.
To enrich your computer vision vocabulary we would like to draw distinctions between several terms that are sometimes used interchangeably by management folk: An attempt at defining AI is fraught with cultural, conceptual and even philosophical difficulties, among which we can count: Here is a pragmatic definition of AI.
An intelligent agent is in turn any agent that is (mostly): Two corollaries of this definition are that intelligent agents necessarily comprise (and can almost be reduced to): Armed with the previous definition, however imperfect, we are better equipped to review and understand some of the sub-fields of AI.
To put some order in our ideas we also attempted a precise and practical definition of AI, that focuses on observable and objectively quantifiable goals rather than on hard questions about the mind and the true meaning of intelligence.
Compliant Database DevOps
Artificial Intelligence (AI) has been in the limelight in recent years with self-driving cars, its most famous application.
Although experiments with AI began in the 1950s, the latest advancements in machine learning proves it’s emerging as a disruptive technology.
Several companies in a variety of domains are trying to find ways to utilize AI agents or bots to automate tasks and improve productivity.
This evolution was primarily triggered by shifting of focus from reducing testing timelines to increasing test coverage and effectiveness of testing activities.
However, the level of automation of software testing processes are still very low due to two main reasons: This is going to change with the adoption of AI, analytics, and machine learning in testing.
Some of the common applications of AI in our daily lives are speech recognition and translation in the form of bots like Siri and Alexa and autonomous vehicles, including self-driving cars as well as spacecraft.
During the early periods of AI development, scientists and researchers understood that it is not necessary for them to emulate the human mind completely to build autonomous learning or make computers think.
Since the 1950s, AI has been experimented with in various forms by fields like computational linguistics for natural language processing, control theory to find the best possible actions, and machine evolution or genetic algorithms to find solutions to problems.
There are primarily three main ways, computers learn: In supervised learning, a computer is provided with labeled data so that it can learn what distinguishes each feature.
Another example would be to use insurance data to predict the range of losses the company could make from the insurance product, given a set of attributes of a person.
This method of learning is used in a lot of applications like clustering (grouping similar objects together and finding point of separation between distinct ones), association (finding relations between objects) and anomaly detection (detecting unnatural behavior in the normal operation of a system).
It uses an iterative process to perform actions, receive feedback because of those actions and an evaluation of the feedback to see if the action was positive or negative.
The main objectives with any QA or testing efforts are to: Hence with testing AI products and deliverables, the software tester could use traditional testing tools, or use AI-enabled testing tools.
Predictive analytics in software testing is simply the use of predictive techniques like object detection and identification to perform steps in test execution.
Now that you have gained an understanding of the capabilities of AI and its implications in testing, take a look at some examples of how testing tools are utilizing AI to empower testers.
Automation tools gained prominence across the software industry aiming to reduce repetitive manual work, increase efficiency and accuracy of test execution, and perform regular checks by scheduling jobs to run on a release schedule.
It can be used to automate mobile, desktop and web applications and supports the creation of smoke, functional, regression, integration, end-to-end, and unit test suites.
Once the test case has been recorded, multiple conditions can be added as follows: Once the test cases are created and ready to be executed, following options can be used to execute them: When it comes to validating the property of an object present on the UI, TestComplete provides multiple options to access the object’s properties.
However, reading content on images or a graphical chart like interfaces which are becoming more common with the proliferation of business intelligence and data-driven dashboards is difficult to identify and validate or perform automated actions on them.
Figure 1.1 Test Scenario#1 On selecting a bar from the bar chart Who had the most tweets?, the scatter plot Whose retweets were most popular should also show the plot for the same user (validating username).
Figure 1.2 Record a step to click on the User Screen Name textbox, enter the text tableau, hit the enter button and click on the checkbox for the tableau option from the filter drop-down:
TestComplete also has a provision to use external packages for Python and other scripting languages which can be leveraged to include other machine learning models for object detection, translation, audio to text, etc.
functional testing to a more lifecycle automation approach, it is imperative that robotic automation and cognitive automation will become a norm in the market soon.
Understanding How Blockchain Can Make Artificial Intelligence Safer and Smarter
As I wrote in my article: Understanding The Gold Rush of Scalable and Validated Data powered by Blockchain and Decentralized AI for Hackernoon: In scripted environments like video games, you can train an AI based on a limited number of pre-defined actions, reading the code of the game and in this kind of environment, machine learning algorithms can make decisions based on that.
You can create algorithms that are smarter enough to recognize some of them, but, remember in this case you need a lot, I mean “A LOT” of computational power to run every calculation, so at the end of the day it’s incredibly expensive and at the same time with a vast percentage of errors.
As Jeremy Epstein wrote in an article for Venture Beat “Why you want blockchain-based AI, even if you don’t know it yet”: If you’re a company or you’re running a country, or even if you’re a casual user, before trusting an AI and use it to make a decision or to start automatic AI-based decisions making, you have to ask yourself: How we can trust AI algorithms, if we’re training these algorithms with a manipulable database, in environments were hackers or owners can edit the data for whatever reasons.
As Maria Korolov argued in her article “AI’s biggest risk factor: Data gone wrong” for CIO Magazine interviewing different AI’s engineers If you’re the owner of the dataset and someone makes decisions based on your database, technically you’re ruling him, because you can manipulate the data and on consequence manipulate the choices of others.For example today we’re trusting big tech companies and their datasets because they are for good and they didn’t have any economic incentive to manipulate pieces of information.
If we start collecting a massive amount of clean and decentralized data we’ll make a huge step forward in terms of AI’s trust and we will be able to start using it for business choices, ensuring a future as far as we can from a “1984 Scenario” dependent from people that rule big corporations.
- On 27. februar 2021
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