Data points that are unusually far apart from the rest of the observations in a dataset are known as outliers. They are primarily caused by data errors (measurement or experimental errors, data collection or processing errors, and so on) or naturally very singular and different behaviour from the norm, for example, in medical applications, very few people have upper blood pressure greater than 200, so If we keep them in the dataset, our statistical analysis, and modelling conclusions will be skewed. To name a few, they can alter the mean and standard deviation values. As a result, it’s critical to accurately detect and handle outliers, either by removing them or reducing them to a predefined value. Outlier detection is thus critical for identifying anomalies whose model predictions we can’t trust and shouldn’t use in production. The type of outlier detector that is appropriate for a given application is determined by the data’s modality and dimensionality, as well as the availability of labelled normal and outlier data and whether the detector is pre-trained (offline) or updated online.
Computer vision has progressed from an experimental technology to one that can interpret patterns in images and classify them using machine learning algorithms to scale. Advances in deep learning and neural networks enable computer vision uses to increase for enterprises, improving worker safety in the process. Computer vision techniques to reduce worker injuries and improve in-plant safety are based on unsupervised machine learning algorithms that excel at identifying patterns and anomalies in images. Computer vision platforms, including Everguard’s SENTRI360, rely on convolutional neural networks to categorize images and industrial workflows at scale. The quality of the datasets used to train supervised and unsupervised machine learning algorithms determines their accuracy. Convolutional neural networks also require large amounts of data to improve their precision in predicting events, fine-tuned through iterative cycles of machine learning models. Each iteration of a machine learning model then extracts specific attributes of an image and, over time, classifies attributes.
Undoubtedly, it needs a complete overhaul of the existing policies, but what we must not forget is that in an evolving environment nothing can be treated as constant. Therefore, swift revision of new policies is very important to match up to the changing scenarios while maintaining people centricity as the central thought. Knowing the employee pulse will be the key to create or revise policies for which regular surveys, town halls, leadership connections are extremely important. Employee safety and well-being will continue to hold the top of mind space and the inclination of workplace culture transformation would be towards empathy and flexibility. Though the challenge of overcoming ‘how-much-is-too-much’ is something that the organizations would need to solve for. They will have to rally together to find the sweet spot to maintain the right balance between productivity and not hampering the work life balance of the employees. ... If data is considered the new oil of the 21st century, ‘Trust’ will become equivalent to it in the post pandemic world making the relationship between the employer and the employee go through a gradual transition where managing expectations from both ends will be essential.
Blockchain technologies, something we have been discussing for a few years, are closer than we think. Transparency, traceability, and sustainability are vital to everyone in the industry. The FDA has outlined four core elements in the New Era of Smarter Food Safety Blueprint, and the first of these elements is tech-enabled traceability. Traceability processes are critical to ensure all food items are traced and tracked throughout the supply chain. Traceability is essential for food safety as well as operational efficiency. With a solid traceability program, it is possible to locate a product at any stage of the food chain within the supply chain — literally from farm to table. For this technology to work well, it must be user-friendly and affordable to all — small businesses and large corporations alike. When it is available and widely used, it will minimize foodborne illness outbreaks and assist significantly with speeding up the process of finding the source if an outbreak does occur. Affordable digital technology connecting buyers with validated verified sellers is at the forefront.
I was just a UX person, not a coder. Surrounded by only the most freakishly good developers at Facebook (and then at Stack Overflow), I pushed whatever fantasies I had about coding professionally aside. During these few years in which I’ve been coding in earnest on the side, I also found myself regularly discouraged and confused by the sheer number of possible things that I could learn or do. I can’t count the number of quarter-finished games and barely-started projects I have in my private GitHub repos (actually, I can. It’s 15, and those are just the ones that made it there). Without much formal education in this field, I’d frequently get lost down documentation holes and find myself drowning in the 800 ways of maybe solving the problem that I had. Finally, I came to the conclusion that I needed more structure, and that I wouldn’t be able to get that structure in the hour of useful-brain-time I had after work each day. I started researching bootcamps and doing budget calculations and made plans to leave Stack Overflow.
The different analyses and drives with quantum computing by the big tech and other organizations are setting out an ocean of open doors before CIOs and IT offices to apply the innovation into this present reality settings. Quantum computers are undeniably appropriate for settling complex optimization- and performing quick quests of unstructured data, as Prashanth Kaddi, Partner, Deloitte India makes reference to, “it can possibly bring problematic change across areas, including finding, medication research, dispersion store network, traffic flow, energy optimizing and many more. Quantum computing additionally fundamentally diminishes time to market, just as helps in enhancing customer delivery. For instance, a drug organization may essentially decrease an opportunity to showcase new medications. In finance, it could empower quicker, more complicated Monte Carlo stimulations, like trading, trajectory optimisation, market instability, and value advancement procedures, and some more.
Just one trifling issue: Encoding or decoding data in antiferromagnets can be a bit like trying to write with a dried-up pen or decipher the scribblings of a toddler. "The difficulty—and it's a significant difficulty—is how to write and read information," said Tsymbal, George Holmes University Professor of physics and astronomy. The same antiferromagnetic property that acts as a pro in one context—the lack of a net magnetic field preventing data corruption—becomes a con when it comes to actually recording data, Tsymbal said. Writing a 1 or 0 in a ferromagnet is a simple matter of flipping its spin orientation, or magnetization, via another magnetic field. That's not possible in an antiferromagnet. And whereas reading the spin state of a ferromagnet is similarly straightforward, it's not easy distinguishing between the spin states of an antiferromagnet—up-down vs. down-up—because neither produces a net magnetization that would yield discernible differences in the flow of electrons. Together, those facts have impeded efforts to develop antiferromagnetic tunnel junctions with practical use in actual devices.
IoT devices and AI are assisting fisheries in optimizing where and when they fish, as well as there are sensors that identify fish and catch size and onboard cameras aid in sorting the catch. The data can also assist the wild fishing industry cut expenses by providing insights on how to cut fuel usage and enhance fleet maintenance using AI predictive maintenance. According to McKinsey, if large-scale fishing enterprises around the world adopt this concept, they may save $11 billion in annual operating costs. With feed accounting for a considerable amount of operating costs and both under and overfeeding having severe consequences for fish health and size as well as water quality, feeding optimization can result in significant savings and benefits. Many smart fish farms, such as efishery, use feeders that rely on vibration and auditory cues to help with more accurate feeding. The use of technology in aquaculture management is also boosting efficiencies and reducing manpower demands, which is a significant financial and safety benefit for remote marine farms.
Companies want to become more advanced in analytics in order to better compete. Yet, they are struggling with both keeping talent in house and building new talent to perform more advanced analytics. In other words, organizations need to build literacy to utilize tools such as self-service BI, and they need to either retain or grow talent to move forward with data science. Data literacy was a priority in 2021 and it will continue to be in 2022. Data literacy involves how well users understand and can interact with data and analytics and communicate the results to achieve business goals. It includes understanding data elements, understanding the business, framing analytics, critical interpretation, and communication skills. As part of this, we expect to see more organizations building literacy enablement teams to help educate their people. We expect to see modern analytics tools with more advanced and augmented features such as natural language search and the ability to surface descriptive insights becoming more popular.
Quote for the day:
"The best leader brings out the best in those he has stewardship over." -- J. Richard Clarke