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Eliminating AI Bias


The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine's ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. As AI algorithms can have long-term impacts on an organisation's reputation and severe consequences for the public, it is important to ensure that they are not biased towards a particular subgroup within a population. In layman's terms, algorithmic bias within AI algorithms occurs when the outcome is a lack of fairness or a favouritism towards one group due to a specific categorical distinction, where the categories are ethnicity, age, gender, qualifications, disabilities, and geographic location. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. AI Bias takes place when assumptions are made incorrectly about the dataset or the model output during the machine learning process, which subsequently leads to unfair results. Bias can occur during the design of the project or in the data collection process that produces output that unfairly represents the population. For example, a survey posted on Facebook asking about people's perceptions of the COVID-19 lockdown in Victoria finds that 90% of Victorians are afraid of travelling interstate and overseas due to the pandemic. This statement is flawed because it is based upon individuals that access social media (i.e., Facebook) only, could include users that are not located in Victoria, and may overrepresent a particular age group (i.e. To effectively identify AI Bias, we need to look for presence of bias across the AI Lifecycle shown in Figure 1.

Tesla Working on Full Self-Driving Mode, Extending AI Lead - AI Trends


Tesla's goal to release its level 5 Full Self Driving (FSD) mode autopilot capability in 2021 was deemed unrealistic by the CEO of competitor Waymo in a recent interview. Tesla is the only autonomous vehicle manufacturer using real-time cameras, rather than pre-mapped Lidar (Light Detection and Ranging) to guide vehicle movement. Tesla also uses its own AI chips, developed after early experience with NVIDIA chips. "It is a misconception that you can simply develop a driver-assistance system further until one day you can magically jump to a fully autonomous driving system," stated John Krafcik, CEO of Waymo, the self-driving startup spun off from Google's X lab, in a recent interview with German business magazine Manager Magazin, reported in Observer. Krafcik acknowledged that Tesla "is developing a really good driver assistance system," but very different.

Pinaki Laskar on LinkedIn: #AI #DeepLearning #Machinelearning


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What type of #AI generates something new from data it is fed? It might be the third wave of Artificial Human Intelligence, dubbed as Neuro-Symbolic AI using #DeepLearning to boost the Symbolic AI approach, and vice versa, by combining logic and learning to transcend both limitations. In terms of Deep Learning, some of the issues are as follows, #Machinelearning requires a massive amount of data to train neural networks, which is not easy to get every time. Selecting the right algorithm is crucial as the results may be biased and lead to a bad prediction. They lack the ability to generalize and are bound by their training data i.e. there is a lack of creativity and they are only efficient at what they already know.

Google AI: Utilizing Artificial Intelligence to Provide Efficiency to the World


Being the Silicon Valley hi-tech giant, Google has started implementing cutting-edge technologies like artificial intelligence to provide efficiency to the world. Google AI conducts research to advance the state-of-the-art through AI-based software. AI at Google develops artificial intelligence tools to ensure that the world can access the strong and smart functionalities of AI. The mission of Google AI is to organize the real-time information and make it accessible to the world for multiple different useful purposes for each and every sector. The implementation of artificial intelligence has offered Google Translate, Google Assistant, and many more with new ways of solving real-life complicated problems.

How Artificial Intelligence is Improving Cloud Computing? - GeeksforGeeks


The first two decades of the 21st century have seen exponential advancements in technologies that were once considered elements solely belonging to a sci-fi movie script. The information age saw the genesis of many such technologies, some of which never saw the light of the day. But two technologies that stood in their time and have now become staples today are Artificial Intelligence and Cloud Computing. In this article, we'll take a look at what these two technologies are and how their amalgamation is proving to be a landscape-changing force in the world of modern technology. Simply stated, Artificial Intelligence is the simulation of human intelligence by machines.

How do we know AI is ready to be in the wild? Maybe a critic is needed


Mischief can happen when AI is let loose in the world, just like any technology. The examples of AI gone wrong are numerous, the most vivid in recent memory being the disastrously bad performance of Amazon's facial recognition technology, Rekognition, which had a propensity to erroneously match members of some ethnic groups with criminal mugshots to a disproportionate extent. Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy? "This is a really good question, and one we are actively working on," Sergey Levine, assistant professor with the University of California at Berkeley's department of electrical engineering and computer science, told ZDNet by email this week. Levine and colleagues have been working on an approach to machine learning where the decisions of a software program are subjected to a critique by another algorithm within the same program that acts adversarially.

INSPIRE-5Gplus insights on TinyML for IoT presented at MLIS 2021


At the 3rd International Conference on Machine Learning and Intelligent Systems (MLIS 2021), which was held online from 8th to 11th November 2021, Dr. Ramón Sánchez Iborra from INSPIRE-5Gplus partner University of Murcia gave an invited talk on the latest insights of the project in the area of Tiny Machine Learning (TinyML). TinyML is an embedded software technology that integrates reduced and optimized machine learning applications for making computing at the edge less expensive and more effective. The talk entitled "TinyML: A groundbreaking shift for the Internet of Things" explored how the dawn of the TinyML paradigm has brought a new wave of capabilities to new and already deployed Internet of Things (IoT) infrastructures. Concretely, TinyML permits to embed powerful ML mechanisms within resource-limited end-devices, hence evolving these elements into truly intelligent units. Besides, the conjunction of TinyML and edge-computing architectures paves the way for the development of distributed-intelligence systems.

Why Java is the Most Preferred for Artificial Intelligence -


AI has brought digital transformation into business operations across various industries. It has become a significant part of our lifestyle. We can offer many use cases where Artificial Intelligence simplifies the process workflow, from autopilots for self-driving cars to using robots to handle warehouse jobs, implementation of chatbots in the customer care portals and more. The Artificial Intelligence technology implications for the purpose of business processes in different sectors are enormous. That is why the purpose and need for hiring skilled java developers to build AI-based apps is skyrocketing in recent years.

Cellphone and tech clues that your partner is cheating on you

FOX News

An easy way to keep two romantic lives separate is to buy two separate phones. That way, the cheater doesn't get confused and text the wrong person by mistake. A second phone is also a liability, even if expressed as a "work" or "emergency" phone. Another technique is to purchase a separate SIM card. Some phones allow you to have two SIM cards but that can be a hassle. A much easier way is to get a Google Voice number that rings on the current phone. In this photo illustration, Apple's iPhone 12 seen placed on a MacBook Pro.

'AI Will Revolutionize Every Aspect of Connectivity,' Argue Cyber Experts


"AI will revolutionize every aspect of connectivity," was the bold message delivered during a recent webinar by the IDC titled'AI with everything – the future of Artificial Intelligence in Networking.' The synopsis of the webinar argued that artificial intelligence (AI) is changing how networks are built and operated in the most profound of ways. Additionally, IT professionals are more reliant than ever on networks to keep enterprises agile, secure and competitive. As a result, advanced tools are needed to keep networks running at optimal levels. AI plays a critical part in making network operations simpler, smarter, more secure and faster.