Collaborating Authors


Transforming the automotive supply chain for the 21st century

MIT Technology Review

For the JIT model to work, the quality and supply of raw materials, the production of goods, and the customer demand for them must remain in alignment. If any one of the links in the chain breaks, stalls, or falls out of sync, the impact on the supply chains that crisscross the world can be felt immediately. For companies, unable to deliver on orders in a timely fashion, they risk losing not only efficiency gains but also brand credibility, market share, and revenue. Now, companies are seeking new ways of managing their supply chains that offer greater flexibility and transparency. In the automotive sector, some companies including Nissan and JIT pioneer Toyota are increasing chip inventory levels, while others including Volkswagen and Tesla are trying to secure their own supplies of rare metals.

Tools for ML Experiment Tracking and Management


We are a group of researchers from Sweden, Netherlands, and Germany and kindly invite you to our survey on "Machine Learning Experiment Management Tools." Such tools support practitioners performing machine learning (ML) or deep learning (DL) experiments, systematically managing all involved artifacts (scripts, datasets, hyperparameters, models, …). As a machine learning practitioner, we kindly invite you to participate. We also invite you to forward this invitation to other colleagues who might be interested in this survey as well. Our survey elicits information on the management tools practitioners adopt, their perceived benefits, challenges, and limitations.

Is Predict GmbH - Google Search


Our company name shows our passion: Predictive analytics - always aiming at increasing process efficiency for humans, machinery, material and electricity. Therefore, we have realized Self-learning Predictive Intelligence software solution which automates time consuming Data Science work with the help of Artificial--... Company Description: IS Predict GmbH is located in Saarbr--cken, Saarland, Germany and is part of the Computer Systems Design and Related Services Industry. She is co-founder and managing director of AI company IS Predict which is automating Data Science for Industry 4.0 with its--... IS Predict GmbH ... IS Predict is a leading Artificial Intelligence (AI) solutions provider for Industrial IoT. IS Predict GmbH ... PREDICTIVE INTELLIGENCE is an unsupervised, self-learning analysis, prediction and control solution. Employees, 7 ( View all); Founded, 2010; Category, Consumer Electronics & Computers, Retail; Web Rank, 22 Million; Keywords, is predict gmbh, universit--t--... IS Predict GmbH, Saarbr--cken, Germany, District Court of Saarbr--cken HRB 18868: Earnings, Public funding, Total assets, Revenue, Network,--...

AI and Machine Learning Coordinator : Reading, UK or Bonn, Germany


Over the last decade, artificial intelligence (AI) and machine learning (ML) techniques have developed at an unprecedented pace, and it is now evident that many scientific disciplines can hugely benefit from these developments provided they explore more data centric methodologies. The science community is currently exploring how the new AI and machine learning techniques can be exploited to further enhance our Earth-system prediction capabilities and first results show exciting potential. However, the scope and speed of these AI/ML developments also generate challenges for weather and climate modelling centres such as ECMWF. These challenges regard the necessary knowledge that needs to be established, the software and hardware infrastructures that need to be developed and used, and the integration of machine learning and conventional tools across the entire prediction workflows, which are continuously evolving. It is fundamental that these challenges are addressed and that the weather and climate modelling community and ECMWF's Member and Co-operating States are enabled to make the best possible use of machine learning in the years to come.

La veille de la cybersécurité


Artificial intelligence (AI) is showing promising results in detecting breast cancer which may otherwise have been missed by radiologists, the largest study of its kind has found. Researchers in Germany discovered that AI can correctly detect interval breast cancers, which develop in between routine screening rounds (usually 24 months in many countries) and can be missed and diagnosed as a false negative result. In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths globally, according to the World Health Organization (WHO). The peer-reviewed study showed approximately 16 per cent of interval cancers are probably visible during a previous screening while one in five may be too subtle to the human eye and can be missed by radiologists, which is known as'minimal signs'. The findings present an opportunity to detect more cancers at screening with AI, which may help detect breast cancer earlier.

How AI can help fight misinformation - ITU Hub


Disinformation has become a global problem affecting citizens, governments and businesses. Identifying and isolating so-called "fake news" poses a major challenge across today's growing digital information ecosystem. But advances in artificial intelligence (AI) could increasingly help online information users sort out fact from fiction. The Global Disinformation Index (GDI) collects data on how misinformation – or disinformation, when deliberate – travels and spreads. The index, put out by a US-based non-profit organization, can help governments, media professionals, and other web users assess the trustworthiness of online content.

Automation Isn't the Biggest Threat to US Factory Jobs


The number of American workers who quit their jobs during the pandemic--over a fifth of the workforce--may constitute one of the largest American labor movements in recent history. Workers demanded higher pay and better conditions, spurred by rising inflation and the pandemic realization that employers expected them to risk their lives for low wages, mediocre benefits, and few protections from abusive customers--often while corporate stock prices soared. At the same time, automation has become cheaper and smarter than ever. Robot adoption hit record highs in 2021. This wasn't a surprise, given prior trends in robotics, but it was likely accelerated by pandemic-related worker shortages and Covid-19 safety requirements.

Insurance Companies Using AI to Build Safety Systems, Optimize Rates - AI Trends


INSHUR is aimed at helping rideshare drivers using Uber or Lyft, and limousine drivers, to find competitive rates for auto insurance. Founded in 2016, the company is based in New York City, is backed by Munich Re Digital Partners, and launched in the UK in 2018. INSHUR has signed up over 40,000 drivers. The company supports liability and physical damage policies with minimum limits of insurance as required by the NYC Taxi & Limousine Commission (TLC) for limousines, which is also compatible with requirements for ride sharing services.

German AI-Startup Landscape: 2022 - appliedAI


The AI startups included in the landscape are private companies founded after 2012, with headquarters or significant operation in Germany. They have machine learning (ML) at their core or exhibit a significant usage of ML. During the year startups apply to be featured on the Landscape via our online survey. This year we received 94 new applications.Startups are then evaluated based on data, talent, AI methods, scalability, overall quality and subsequently clustered (see clustering logic).The startups are initially rated ('shortlisted', and'discarded') by our AI Engineers and Strategists to create a shortlist. Startups from previous year landscape are automatically transferred to the new year iteration unless they have closed their business, were acquired, pivot their business model away from AI or moved to a different geographical location.

What's next for AlphaFold and the AI protein-folding revolution


For more than a decade, molecular biologist Martin Beck and his colleagues have been trying to piece together one of the world's hardest jigsaw puzzles: a detailed model of the largest molecular machine in human cells. This behemoth, called the nuclear pore complex, controls the flow of molecules in and out of the nucleus of the cell, where the genome sits. Hundreds of these complexes exist in every cell. Each is made up of more than 1,000 proteins that together form rings around a hole through the nuclear membrane. These 1,000 puzzle pieces are drawn from more than 30 protein building blocks that interlace in myriad ways. Making the puzzle even harder, the experimentally determined 3D shapes of these building blocks are a potpourri of structures gathered from many species, so don't always mesh together well. And the picture on the puzzle's box -- a low-resolution 3D view of the nuclear pore complex -- lacks sufficient detail to know how many of the pieces precisely fit together. In 2016, a team led by Beck, who is based at the Max Planck Institute of Biophysics (MPIBP) in Frankfurt, Germany, reported a model1 that covered about 30% of the nuclear pore complex and around half of the 30 building blocks, called Nup proteins.