Among the factors of production, human capital or skilled manpower is the one that keeps evolving and adapts to changing conditions and resources. This adaptability makes human capital the most crucial factor in ensuring a sustainable growth of industry/sector. As new technologies are developed and adopted, the new generations are required to acquire skills in newer technologies in order to be employable. At the same time professionals are required to upskill and reskill themselves to remain relevant in the industry. There is however no straightforward method to identify the skill needs of the industry at a given point of time. Therefore, this paper proposes a data to decision framework that can successfully identify the desired skill set in a given area by analysing the advertorial data collected from popular online job portals and supplied as input to the framework. The proposed framework uses techniques of statistical analysis, data mining and natural language processing for the purpose. The applicability of the framework is demonstrated on CS&IT job advertisement data from India. The analytical results not only provide useful insights about current state of skill needs in CS&IT industry but also provide practical implications to prospective job applicants, training agencies, and institutions of higher education & professional training.

Since the war began in Gaza, more than six months ago, the Israeli magazine 972 has published some of the most penetrating reporting on the Israel Defense Forces' conduct. In November, 972, along with the Hebrew publication Local Call, found that the I.D.F. had expanded the number of "legitimate" military targets, leading to a huge increase in civilian casualties. Then earlier this month, 972 and Local Call released a long feature called "Lavender: The AI Machine Directing Israel's Bombing Spree in Gaza." The story revealed how the Israeli military had used the program to identify suspected militants, which in practice meant that tens of thousands of Palestinians had their homes marked as legitimate targets for bombing, with minimal human oversight. The I.D.F. also said that, according to its rules, "analysts must conduct independent examinations" to verify the identification of targets.

Whole slide image (WSI) classification is a critical task in computational pathology. However, the gigapixel-size of such images remains a major challenge for the current state of deep-learning. Current methods rely on multiple-instance learning (MIL) models with frozen feature extractors. Given the the high number of instances in each image, MIL methods have long assumed independence and permutation-invariance of patches, disregarding the tissue structure and correlation between patches. Recent works started studying this correlation between instances but the computational workload of such a high number of tokens remained a limiting factor. In particular, relative position of patches remains unaddressed. We propose to apply a straightforward encoding module, namely a RoFormer layer , relying on memory-efficient exact self-attention and relative positional encoding. This module can perform full self-attention with relative position encoding on patches of large and arbitrary shaped WSIs, solving the need for correlation between instances and spatial modeling of tissues. We demonstrate that our method outperforms state-of-the-art MIL models on three commonly used public datasets (TCGA-NSCLC, BRACS and Camelyon16)) on weakly supervised classification tasks. Code is available at https://github.com/Sanofi-Public/DDS-RoFormerMIL

Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.

The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of classes and inter-model comparison in such classification problems are two areas that have not received much attention in the literature, despite the ever-increasing use of classification models to address problems with very large class categories. In this paper, we present our interactive visual analytics tool, called Circles, that allows a visual inter-model comparison of numerous classification models with 1K classes in one view. To mitigate the tricky issue of visual clutter, we chose concentric a radial line layout for our inter-model comparison task. Our prototype shows the results of 9 models with 1K classes

Alphabet and Amazon are among the companies best positioned to take advantage of future artificial intelligence disruption in the technology industry, our analysis shows. The assessment comes from GlobalData's Thematic Research ecosystem, which ranks companies on a scale of one to five based on their likelihood to tackle challenges like artificial intelligence and emerge as long-term winners of the technology sector. The table below shows how GlobalData analysts scored the biggest companies in the technology industry on their artificial intelligence performance, as well as the number of new artificial intelligence jobs, deals and patents from the companies since August 2021. The final column in the table represents the overall score given to that company when it comes to their current artificial intelligence position relative to their peers. A score of five indicates that a company is a dominant player in this space, while companies that score less than three are vulnerable to being left behind.

The government will soon release data on collisions involving vehicles with autonomous or partially automated driving systems that will likely single out Tesla for a disproportionately high number of such crashes. In coming days, the National Highway Traffic Safety Administration plans to issue figures it has been gathering for nearly a year. The agency said in a separate report last week that it had documented more than 200 crashes involving Teslas that were using Autopilot, "Full Self-Driving," Traffic-Aware Cruise Control or some other of the company's partially automated systems. Tesla's figure and its crash rate per 1,000 vehicles was substantially higher than the corresponding numbers for other automakers that provided such data to The Associated Press ahead of NHTSA's release. The number of Tesla collisions was revealed as part of a NHTSA investigation of Teslas on Autopilot that had crashed into emergency and other vehicles stopped along roadways.

You have finished solving a machine learning problem. The accuracy of your model is awesome. Till now your work is probably a Jupyter notebook, which is full of code, a few visuals, and very little documentation. If you see your work after a month or so, you might struggle to understand your own creation. To make matter worse, the Jupyter notebook does not have all decisions and assumptions you have taken in the solution.

You have finished solving a machine learning problem. The accuracy of your model is awesome. Till now your work is probably a Jupyter notebook, which is full of code, a few visuals, and very little documentation. If you see your work after a month or so, you might struggle to understand your own creation. To make matter worse, the Jupyter notebook does not have all decisions and assumptions you have taken in the solution.

Unilever and Suntory are among the beverage brand owners best positioned to take advantage of future artificial intelligence disruption in the industry, according to recent research. The assessment comes from GlobalData's Thematic Research ecosystem, which ranks companies on a scale of one to five, based on their likelihood to tackle challenges like AI. According to the analysis, Unilever is well-placed to benefit from its investments in artificial intelligence. The group, which operates the Lipton iced tea brand in partnership with PepsiCo, was the only company to attain the top score in GlobalData's non-alcoholic beverages'Thematic Scorecard'. In the 12 months to the end of September, Unilever advertised for 323 new artificial intelligence-related roles and mentioned artificial intelligence five times in its filings.