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Three opportunities of Digital Transformation: AI, IoT and Blockchain

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Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).


Amazon's Alexa being tested to replicate voice of dead relatives

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Amazon's Alexa might soon replicate the voice of family members - even if they're dead. The capability, unveiled at Amazon's Re:Mars conference in Las Vegas, is in development and would allow the virtual assistant to mimic the voice of a specific person based on a less than a minute of provided recording. Rohit Prasad, senior vice president and head scientist for Alexa, said at the event Wednesday that the desire behind the feature was to build greater trust in the interactions users have with Alexa by putting more "human attributes of empathy and affect." "These attributes have become even more important during the ongoing pandemic when so many of us have lost ones that we love," Prasad said. "While AI can't eliminate that pain of loss, it can definitely make their memories last."


Expressive Querying for Accelerating Visual Analytics

Communications of the ACM

In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.


Community Marketing Data Scientist

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


Why Python?

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Moreover, Python has various excellent libraries and frameworks that save time and effort.


This data scientist wants to address human issues around AI

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Data scientist Oisín Boydell is working on a project that seeks to democratise access to an ever-increasing volume of Earth observation data. Dr Oisín Boydell is principal data scientist and head of the applied research group at CeADAR, the SFI-funded centre for applied AI at University College Dublin (UCD). His primary research interests include trustworthy AI, deep learning, natural language processing and applications of AI to Earth observation data. After working as a software developer in the UK, Boydell returned to UCD to undertake a PhD in computer science, researching novel approaches for personalised information retrieval. Prior to joining CeADAR he worked with SMEs and multinationals on big data analytics and machine learning solutions for the telecommunications industry.


Data Scientist, Creator Promotion

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Senior Data Scientist

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Data Scientist - Demand Forecasting

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What is unstructured data in AI?

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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. Many databases are filled with information that's carefully organized into rows and columns. The type and role for each part is pre-defined and often enforced by software that checks the data before and after it's stored. Studying these tables for insights is relatively simple and straight-forward for data scientists. Some data sources, though, lack predictable order, but this doesn't mean that they can't be useful.