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Artificial Intelligence and Automated Systems Legal Update (1Q22)

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Secretary shall support a program of fundamental research, development, and demonstration of energy efficient computing and data center technologies relevant to advanced computing applications, including high performance computing, artificial intelligence, and scientific machine learning.").


50 Examples of Machine Learning & AI in Data Analysis

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Analytics has been changing the bottom line for businesses for quite some time. Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more. That's why companies are looking to implement machine learning (ML) and artificial intelligence (AI); they want a more comprehensive analytics strategy to achieve these business goals. Learning how to incorporate modern machine learning techniques into their data infrastructure is the first step. For this many are looking to companies that already have begun the implementation process successfully. For call centers, using ML and AI means having conversation analytics software in place – in fact, decades ago call centers began using primitive forms of artificial intelligence.


The Data Analyst Course: Complete Data Analyst Bootcamp 2022

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This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!


Regulating AI Through Data Privacy

Stanford HAI

In the absence of a national data privacy law in the U.S., California has been more active than any other state in efforts to fill the gap on a state level. The state enacted one of the nation's first data privacy laws, the California Privacy Rights Act (Proposition 24) in 2020, and an additional law will take effect in 2023. A new state agency created by the law, the California Privacy Protection Agency, recently issued an invitation for public comment on the many open questions surrounding the law's implementation. Our team of Stanford researchers, graduate students, and undergraduates examined the proposed law and have concluded that data privacy can be a useful tool in regulating AI, but California's new law must be more narrowly tailored to prevent overreach, focus more on AI model transparency, and ensure people's rights to delete their personal information are not usurped by the use of AI. Additionally, we suggest that the regulation's proposed transparency provision requiring companies to explain to consumers the logic underlying their "automated decision making" processes could be more powerful if it instead focused on providing greater transparency about the data used to enable such processes. Finally, we argue that the data embedded in machine-learning models must be explicitly included when considering consumers' rights to delete, know, and correct their data.


10 startups riding the wave of AI innovation

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale. Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. Vashisth further noted that innovations including edge AI, computer vision, decision intelligence and machine learning will have a transformational impact on the market in coming years. However, while AI-powered technologies are helping to build more agile and effective enterprise systems, they usher in new challenges. For example, Gartner notes that AI-based approaches if left unchecked can perpetuate bias, leading to issues, loss of productivity and revenue.


AI/ML, Data Science Jobs #hiring

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AI/ML, Data Science Jobs #hiring

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If you have forgotten your password you can reset it here.


Data Analyst III - Market Optimization

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Who we areOpenX is a leader in the AdTech industry with an exchange that processes billions of ad requests a day. We have seven offices located around the globe (in the US, the UK, Poland, and Japan). We're focused on unleashing the full economic potential of digital media companies. We do this by making digital advertising marketplaces and technologies that are designed to deliver optimal value to publishers and advertisers on every ad served across all screens. Who we are looking forOpenX Technologies, Inc. is looking for Analysts to join our Market Optimization team in the Data Science Org. Our exchange processes hundreds of billions of ad requests daily, resulting in petabytes of new data daily.


C3 AI announces AI cybersecurity grant recipients

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Did you miss a session at the Data Summit? Today, C3 AI's CEO Thomas Siebel announced the third group of Digital Transformation Institute (DTI) grant recipients to accelerate the development of artificial intelligence (AI) and machine learning (ML) technologies in hopes to combat emerging threats. As an organization, C3 AI provides enterprises with an AI Application platform they can use to create, deploy, and manage AI applications for use cases like fraud detection, network health, supply network optimization, energy management, and more. The grant was established in March 2020 by C3 AI, Microsoft, and some of the world's leading universities. The goal is to engage the world's top scientists through cash grants to encourage them to conduct research and train practitioners on AI, machine learning, Cloud computing, IoT, big data analytics, organizational behavior, public policy, and ethics.


Automating Data Science

Communications of the ACM

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.10 To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions. As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels. The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process.