Ethical AI, in simple words, is to ensure that your AI models are fair, ethical and unbiased.
So how does the bias fit into the model? Suppose you are building an AI model that offers salary suggestions for new hires. As part of building the model, you have taken gender as an indicator of salary. The model is trying to discriminate based on gender. In the past, this bias went through human judgments and various social and economic factors but if you include this part of the new model, it is a recipe for disaster. The whole idea is to build a salary model based on people's experiences and qualifications that are non-partisan.
Take another example of an app that provides restaurant recommendations to the user and allows the user to book a table. The AI application is designed to look at the amount spent on previous transactions and restaurant ratings (along with other features), and the AI system begins recommending expensive restaurants. While there are good low-cost restaurants nearby, those restaurants may not be one of the top recommendations. Also, the amount of money the user spends represents more revenue per restaurant application. So in short, you are driving the consumer class towards spending more in high-end restaurants without the consumer knowing about it. Is it classified as a bias or smart income-generating scheme?
Ethical AI is a great topic of research and debate, as you will see a lot of development (as well as general marketing buzzwords) and governance in this area.
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So how is your model ethical and how do you validate it?
Designing a Model Without Prejudice - Make sure you don't include features that may bias your model. For example, do not include gender when evaluating salary packages. Take the time to verify the data sources and attributes that are used to create the model.
Describe model output - Applications design should be a key design principle in terms of detail. If the user receives the output from the AI algorithm, it must build in the algorithm why the output is displayed and how relevant it is. It should empower users to understand why certain information is being displayed, and turn on/off any priorities associated with the AI algorithm for future recommendations/suggestions.
Verify the model - Verify the model with enough test cases. You will grow many offerings (ethical AI services) in the future around this area. Again, offerings/services should be vertical instead of pure-play horizontal AI services (otherwise it ends up as chatbots hype
Accountability - Ultimately, humans need to look at the product that comes from the AI system and take corrective action for critical tasks. I have not seen machines acquire human intelligence for complex tasks in the future. For example, a doctor should carefully consider the cancer treatment option offered by the AI system, but a fashion website that recommends false products for a consumer is not critical and can be corrected by feedback later.
If we go back to the restaurant app if we design the app and explain the output to the user in keeping with the above guidelines, why are we giving at least 4 levels of recommendation (shown in the application tiles):
- Recommend restaurants based on previous restaurant costs, ratings, and history and customer preferences
- Recommended for highly rated and low-cost restaurants based on ratings, history and customer preferences
- Recommends new restaurants based on user history and user preferences
- System generated recommendations without applying user preferences
The revised app now provides enough evidence to back up the various recommendations and recommendations, and ultimately the choice if the restaurant is left to the user to book a table.
The above is an example of a very simple application, but imagine when deploying in AI industries and in government agencies. Developing and monitoring the artificial intelligence system for ethical principles is critical. The creators and validators of the model (agencies / third party systems, etc.) are very important to ensure that the models of AI are fair, ethical, and unbiased.
Since we are creators and validators of AI systems, we (humans) have a responsibility to ensure that technology is used for good.
If you’ve attended any digital marketing or eCommerce conference this year, you know that artificial intelligence is one of the hottest topics around.
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Machine learning: The robot can make its own rules and improve the algorithm to get better results.
Machine learning is often considered a subset of artificial intelligence.
AI for commerce is affordable and easy The next step in understanding how artificial intelligence can help your eCommerce strategy is to eliminate the misconception that investing in AI is expensive and complicated.
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OpenAI is looking forward to this feat for both robotic add-ons and its own AI software, which allows Docktail to learn new things using virtual simulations, before overcoming a real, physical challenge.In a performance video featuring Docktail’s new talent, the robotic arm with clumsy yet precise maneuvers leads to a complete cube solution.
“Velander has been referring to a range of robots for the past few years that have pushed the Rubik’s Cube solution beyond the confines of human hands and minds.
In 2016, semiconductor manufacturer Infineon specifically developed a robot to solve the Rubik’s Cube at superhuman speed, and the bot was able to do so in under a second.
As Wallander notes, OpenAI’s ongoing robotics work is not only about achieving superior results in narrow tasks but also because you need to develop and program a good robot.
It has been trained using software that is currently trying to be fundamental to reflect the millions of years of evolution that help children learn to use our hands naturally.
One day, OpenI’s hopes will help mankind develop the humanoid robots we know only from science fiction, robots that can safely operate in society without risk, and perform a variety of tasks in a chaotic environment such as city streets and factory floors.To learn how to solve a Rubik’s Cube with one hand, OpenIII is not explicitly programmed by Docktail to fix the toy; Free software on the Internet can do it for you.
Resolving the problem of bias and variation is only about coping with over-fitting and under-fitting.
Bias is minimized, and the variance concerning the complexity of the model is increased.Read Full Article: https://bit.ly/2NsIaF4
Artificial intelligence matches human performance by learning, deriving conclusions on its own, and executing a clearly defined task.
Machine Learning is an artificial intelligence technology that uses data to automatically discover patterns and trends, resulting in deriving more accurate predictions of future events.Many organizations are leveraging ML and AI to advance their product development, sales, and overall customer experience.
Industries like manufacturing, construction, energy etc.
Most companies understand the value of a safety culture and have ideologies about what a hyper-vigilant safety culture looks like — where employees are on high alert of their surroundings, perpetually evaluating risks for the task at hand and taking precautions accordingly.
Algorithms can identify objects, edges, and velocity because of the advancements of Machine Vision.Workplace Safety Solutions through AI Computer Vision:Process and Production Safety - Majority of the workplace facilities have a CCTV system installed on the premises.
Vision-based object detection and tracking use feature such as shape, color, motion etc.
IntroductionArtificial Intelligence and data wisdom are good fields of operations, systems, and further that end at replicating mortal intelligence through machines.
AI represents action-planned feedback of perception.Data Science uses different corridors of this pattern or circle to interrupt specific problems.
Perception, data scientists attempt to identify patterns with the assistance of the info.
planning, there are two aspects Artificial Intelligence Artificial Intelligence (AI) is that the reproduction of human knowledge measures by machines, particularly PC frameworks.
Explicit utilizations of AI contain master frameworks, normal language preparing, discourse acknowledgment, and machine vision.Artificial Intelligence is critical because it can give endeavors experiences into their activities that they could not have known about already and since, now and again, AI can execute assignments better than people.
Especially with regards to dull, thorough assignments like examining enormous quantities of authoritative archives to ensure significant fields are filled in appropriately, AI apparatuses regularly complete positions rapidly and with somewhat a few blunders.Data scienceData science, analytics, and machine learning demand are growing at an astronomical rate and top companies are now trying to find professionals in Data science who can sift through the goldmine of knowledge and help them to require business decisions efficiently.Data science may be a concept wont to tackle big data and includes data cleansing, preparation, and analysis.
