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Eliminating AI Bias

#artificialintelligence

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.



Converging the physical and digital with digital twins, mixed reality, and metaverse apps

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Cloud and edge computing are coming together as never before, leading to huge opportunities for developers and organizations around the world. Digital twins, mixed reality, and autonomous systems are at the core of a massive wave of innovation from which our customers already benefit. From the outside, it's not always apparent how this technology converges or the benefits that can be harnessed by bringing these capabilities together. This is why at Microsoft Build we talk about the possibilities this convergence creates, how customers are already benefitting, and our journey to making this technology easier to use and within reach of every developer and organization. Imagine taking any complex environment and applying the power of technology to create awe-inspiring experiences and reach new business heights that were previously unimaginable.


Flying, insect-like robot flits closer to independent flight

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Just over six years ago, when researchers at Harvard announced that they had made tiny flying robots, they immediately began talking about the prospect of their tiny creations operating autonomously in complicated environments. That seemed wildly optimistic, given that the robots flew by trailing a set of copper wires that brought power and control instructions; the robots were guided by a computer that monitored their positions using a camera. Since then, however, the team has continued working on refining the tiny machines, giving them enhanced landing capabilities, for example. And today, the team is announcing the first demonstration of self-powered flight. The flight is very short and isn't self-controlled, but the tiny craft manages to carry both the power supply circuitry and its own power source.


Tesla's Self-Driving Business Is Powering Ahead Despite Setbacks

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Tesla stock (NASDAQ: TSLA) is up by almost 60% year-to-date, with its market cap crossing the rarefied $1 trillion mark recently. The run-up is partly due to Tesla's solid execution, with deliveries for this year poised to grow by almost 70% to about 850,000 vehicles, despite the ongoing semiconductor shortage. Tesla's sizable lead in the self-driving market has also traditionally been a very big driver of the company's valuation. So how far ahead is Tesla's self-driving system versus peers, and how does it stack up versus driver-driven vehicles. See our dashboard analysis on Just How Far Ahead Is Tesla In The Self-Driving Race? for more details.


Digital Twins for Energy Grids

#artificialintelligence

Physical systems, such as electricity grids, are very complex and thereby difficult to model. Digital twins provide a solution. "Digital Twins are one of the top technological trends" Here will we discuss a literature review (2021) that was performed by researchers at Bosch Engineering. The review focused on how digital twin and "big data" technology can be applied to complex physical systems, such as energy grids. Without further ado, let's dive in.


Researchers use AI to optimize several flow battery properties simultaneously

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Scientists seek stable, high-energy batteries designed for the electric grid. Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: The flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy. For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.


The Old Ways Aren't Working: Let's Rethink OT Security

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While attacks against critical infrastructure are nothing new, it is only recently that concerns have spilled into the public view. When Colonial Pipeline was hit by ransomware, for example, there were long lines of cars and panic-buying at gas stations. Some even ran out of supply. Aircraft need jet fuel, and it just quite literally wasn't there anymore," says Dave Masson, director of enterprise security at Darktrace. "And that's pretty much what's woken up public consciousness to this issue." One of the reasons it had been so hard to focus people's attention on threats to critical infrastructure before is the term itself is broad, encompassing many things. "Critical infrastructure is pretty much what makes modern life livable," says Masson. "It's all those things we actually need to live on – how the power turns up, how the water turns on.


Industry collaboration powers new generation of grid emergency control technology

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Grid operators face big challenges and big opportunities when it comes to managing through emergency conditions that disrupt power service. The increasing number of power outages in the United States cost an estimated $30–50 billion and affect millions of customers each year. The challenge and the opportunity both lie in optimizing power system responses when the unexpected happens. Optimization can minimize the effects of these events. Researchers at Pacific Northwest National Laboratory (PNNL) are collaborating with partners at Google Research, PacifiCorp, and V&R Energy to develop a real-time adaptive emergency control system to safeguard the grid against costly disturbances from extreme weather and other disruptive events.


AI and the future of autonomous systems

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Let's consider the case of autonomous racing cars. Berkeley Autonomous Race Car (BARC), Amazon Deep Racer, and others are examples of autonomous racing cars which can be raced effectively without human input.\ The control systems for these cars are enhanced by AI and neural networks. Certain parts of the automation, such as guiding wheels, can be managed by traditional techniques like Model Predictive Control (MPC). However, these traditional techniques are not able to manage the process of driving like a human driver.