Cigniti Technologies, the world's leading AI and IP-led Digital Assurance and Digital Engineering services company, has unveiled a new brand identity reflecting its renewed vision to help its clients in accelerating their digital transformation journeys and achieve market leadership. The new brand identity reflects Cigniti's strengthened resolve to be a trusted digital transformation partner for its clients, including 60 of its Fortune 500 and 80 of its Global 2000 companies, delivering at a global scale with increasingly localized capabilities, and leveraging quality-first digital assurance, product engineering, AI, ML, data and insights, data visualization, automation, and blockchain. In addition to conveying the futuristic vision, the new logo aspires to uphold a contemporary attitude, produce a powerful visual depiction of a shift toward digitalization, and at the same time imbibe the company's software quality-first mindset. Additionally, it aims to exemplify the intense commitment and forward-thinking transformation that the business is embracing through innovation, automation, and artificial intelligence. The company's digital thinking and digital avatar are a reflection of its ability to engineer, assure, and technologically transform and accelerate outcomes for global companies, helping them achieve market leadership in their chosen lines of business.

Last week, researchers at Facebook's parent company Meta released BlenderBot 3, a "publicly available chatbot that improves its skills and safety over time". The chatbot is built on top of Meta's OPT-175B language model, effectively the company's white-label version of the more famous GPT-3 AI. Like most state-of-the-art AIs these days, that was trained on a vast corpus of text scraped from the internet in questionable ways, and poured into a datacentre with thousands of expensive chips that turned the text into something approaching coherence. But where OPT-175B is a general-purpose textbot, able to do anything from write fiction and answer questions to generate spam emails, BlenderBot 3 is a narrower project: it can have a conversation with you. That focus allows it to bring in other expertise, though, and one of Meta's most significant successes is hooking the language model up to the broader internet.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session. Within the past year, according to the document, states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

Even with signs of a recession, hiring continues to be a top priority and challenge for several industries, including healthcare, hospitality, manufacturing, and transportation. There are approximately 11.4 million unfilled jobs in the US, according to recent reports from the US Bureau of Labor Statistics. With the current ratio of one qualified talent professional for every eight open roles, talent teams must find ways to be more efficient and effective. AI enables a quick, efficient hiring process. It also can be a powerful tool to uncover hidden hiring biases and drive change, prompting organizations to assess their historical hiring data and improve recruiting and hiring processes. Data scientists will tell you all data is biased because bias is about finding patterns in data.

Is data science on the decline? Is the data science field overcrowded? Is it too late to pursue a career in data science? Data science and quickly evolving technologies have aided in these transformations and demonstrated that the future is bright. This, however, will be contingent on the quality and scope of data that companies can obtain.

Role requiring'No experience data provided' months of experience in Houston About VLink: Started in 2006 and headquartered in Connecticut, VLink is one of the fastest-growing digital technology services and consulting companies. Since its inception, our innovative team members have been solving the most complex business, and IT challenges of our global clients. Client is looking for a Data Architect who is primarily an individual contributor but can be responsible for a small team. Main scope of work is to provide solution architecture development, consultancy and assurance to projects, making sure applications are well designed and conform to client standards and reference/segment architectures. Translates the guidelines and standards into practice and solves common technical challenges and provides technical recommendations which have a perceptible impact on local business performance; actively drives the identification, development and implementation of new technologies and opportunities to optimise technology/IT systems. May represent the Company externally as a subject matter expert with suppliers, customers and external agencies. Empowered to make decisions on solutions within guidelines. Applies TOE standards and raises step-outs if needed. Understands the IT Strategic Roadmap and applies within the context of their organisational assignment.

Below gives you a sense of how we're thinking about what you'll need to be successful in the role.

Our daily lives are being impacted by artificial intelligence (AI) in several ways. Artificial assistants, predictive models, and facial recognition systems are practically ubiquitous. Numerous sectors use AI, including education, healthcare, automobiles, manufacturing, and law enforcement. The judgments and forecasts provided by AI-enabled systems are becoming increasingly more significant and, in many instances, vital to survival. This is particularly true for AI systems used in healthcare, autonomous vehicles, and even military drones.

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the parameters of interest are functions across this domain. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed-effects relationship between the response variable and the covariates. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire effect of irrelevant regressors. The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (LASSO) penalty function, wherein the weights are obtained by the unpenalised f-HDGM maximum-likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the likelihood. Through a Monte Carlo simulation study, we analysed the performance of the algorithm under different scenarios, including strong correlations among the regressors. We showed that the penalised estimator outperformed the unpenalised estimator in all the cases we considered. We applied the algorithm to a real case study in which the recording of the hourly nitrogen dioxide concentrations in the Lombardy region in Italy was modelled as a functional process with several weather and land cover covariates.

Interventions to increase active commuting have been recommended as a method to increase population physical activity, but evidence is mixed. Social norms related to travel behaviour may influence the uptake of active commuting interventions but are rarely considered in their design and evaluation. In this study we develop an agent-based model that incorporates social norms related to travel behaviour and demonstrate the utility of this through implementing car-free Wednesdays. A synthetic population of Waltham Forest, London, UK was generated using a microsimulation approach with data from the UK Census 2011 and UK HLS datasets. An agent-based model was created using this synthetic population which modelled how the actions of peers and neighbours, subculture, habit, weather, bicycle ownership, car ownership, environmental supportiveness, and congestion affect the decision to trave. The developed model (MOTIVATE) is a configurable agent-based model where social norms related to travel behaviour are used to provide a more realistic representation of the socio-ecological systems in which active commuting interventions may be deployed. The utility of this model is demonstrated using car-free days as a hypothetical intervention. In the control scenario, the odds of active travel were plausible at 0.091 (89% HPDI: [0.091, 0.091]). Compared to the control scenario, the odds of active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the intervention scenario, on non-car-free days; the effect is sustained to non-car-free days. The model is a useful tool for investigating the effect of how social networks and social norms influence the effectiveness of various interventions. If configured using real-world built environment data, it may be useful for investigating how social norms interact with the built environment to cause the emergence of commuting conventions.