The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...

As the Chemistry-Kayak (affectionately known as the ChemYak) swept over the Arctic estuary waters, Victoria Preston was glued to a monitor in a boat nearby, watching as the robot's sensors captured new data. She and her team had spent weeks preparing for this deployment. With only a week to work on-site, they were making use of the long summer days to collect thousands of observations of a hypothesized chemical anomaly associated with the annual ice-cover retreat. The robot moved up and down the stream, using its chemical sensors to detect the composition of the flowing water. Its many measurements revealed a short-lived but massive influx of greenhouse gases in the water during the annual "flushing" of the estuary as ice thawed and receded.

As part of the lead-up to Transform 2021 coming up July 12-16, we're excited to put a spotlight on some of our conference speakers who are leading impactful diversity, equity, and inclusion initiatives in AI and data. We were lucky to land a conversation with Huma Abidi, senior director of AI software products and engineering at Intel. She spoke about her DE&I work in her private life, including her support for STEM education for girls in the U.S. and all over the world, founding the Women in Machine Learning group at Intel, and more. HA: This one is easy. I lead a globally diverse team of engineers and technologists responsible for delivering world-class products that enable customers to create AI solutions.

Machine learning is gaining popularity across scientific and technical fields, but it's often not clear to researchers, especially young scientists, how they can apply these methods in their work. In many ways, ESS present ideal use cases for ML applications because the problems being addressed--like climate change, weather forecasting, and natural hazards assessment--are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so. An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets).

Japan's catastrophic Fukushima disaster in 2011 has resulted in a unique species of boar-pig, a new study reveals. Researchers investigating the effects of the nuclear disaster on animals in the area report that radiation has had no adverse effects on their genetics. However, wild boars (Sus scrofa leucomystax) have proliferated in the area, after being left to roam freely from the lack of humans. The boars have bred with domestic pigs (Sus scrofa domesticus) that escaped from nearby properties after farmers had to flee, creating a new hybrid species. Rare spotted wild boar observed inside the evacuated area of Fukushima, Japan, indicative of the'introgression' - the transfer of genetic information from one species to another - with domestic pigs Images from remotely-operated cameras indicate wildlife is flourishing in Fukushima's exclusion zone. Wildlife ecologist James Beasley of the University of Georgia and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area over a four-month period.

The Navy, through its Office of Naval Research, is pursuing artificial intelligence applications across a broad spectrum of the service's responsibilities to man, train and equip, as well as warfighting, sustainment and readiness. Such a wide range of applications and algorithms come with specific data requirements and data management. Curtis Pelzer, chief information officer at the Office of Naval Research, said ONR's data resides in in several places on their network, and it's the job of the data and analytics team to make sure information is provided and kept in the right sets. AI can help reduce toil across the Navy, give autonomy with unmanned systems, and software codes can increase the speed and quality of human decision-making, according to Brett Vaughan, the Navy's chief artificial intelligence officer and Office of Naval Research portfolio manager. Vaughan said any data could potentially fuel AI, but it depends on what problem one aims to solve.

It took Michelle Anderson a month to create her E3 2019 outfit. It took her another hour to put it on. She wore a wig with red Afro puffs, an army-green tactical vest and fake bloodstained bandage. She completed the look with medical gloves and a mask looped around her neck, then took one last look in the mirror before she headed out the door. She was dressed as Lifeline, a playable combat medic from the video game "Apex Legends."

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.

Military applications of artificial intelligence, we are told, are poised to transform military power. They might make the oceans transparent to sensor systems, threatening at-sea nuclear deterrent systems like the UK's Trident. They might enable autonomous aircraft that could outfight human crewed planes. They could transform intelligence processing in war, enable all sorts of complex weapons that would make things like tanks and aircraft carriers yesterday's news. The sky, it appears, is the limit. In this light, big states are making large investments in military AI. One aspect of the UK's recent Integrated Review (ahem, "Global Britain in a Competitive Age") and Command Paper (ahem, "Defence in a competitive age") is a bet that investment in military applications of artifical intelligence will offset cuts to things like tanks and troop numbers.

Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has previously been employed is the Dynamic Mode Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which fits linear dynamical models to data. While the DMD usually approximates non-linear terms in the true dynamics as a linear system with random noise, we investigate an extension to the DMD that explicitly represents the non-linear terms as a neural network. Our weight initialization allows the network to produce sensible results before training and then improve the prediction after training as data becomes available. In this short paper, we evaluate the proposed architecture for simulating global sea surface temperatures and compare the results with the standard DMD and seasonal forecasts produced by the state-of-the-art dynamical model, CFSv2.