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Pinaki Laskar on LinkedIn: #AI #DeepLearning #Machinelearning


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What type of #AI generates something new from data it is fed? It might be the third wave of Artificial Human Intelligence, dubbed as Neuro-Symbolic AI using #DeepLearning to boost the Symbolic AI approach, and vice versa, by combining logic and learning to transcend both limitations. In terms of Deep Learning, some of the issues are as follows, #Machinelearning requires a massive amount of data to train neural networks, which is not easy to get every time. Selecting the right algorithm is crucial as the results may be biased and lead to a bad prediction. They lack the ability to generalize and are bound by their training data i.e. there is a lack of creativity and they are only efficient at what they already know.

Cellphone and tech clues that your partner is cheating on you

FOX News

An easy way to keep two romantic lives separate is to buy two separate phones. That way, the cheater doesn't get confused and text the wrong person by mistake. A second phone is also a liability, even if expressed as a "work" or "emergency" phone. Another technique is to purchase a separate SIM card. Some phones allow you to have two SIM cards but that can be a hassle. A much easier way is to get a Google Voice number that rings on the current phone. In this photo illustration, Apple's iPhone 12 seen placed on a MacBook Pro.

#AI ( #人工知能 )と #画像認識技術 の進化とニュースまとめ


かき揚げうどんと言えば 僕ってイメージが付いた イーンスパイアの横田です。 昨夜も群馬の高崎駅で食べた ワサビ入り野沢菜かき揚げ、 旨かったですよ(笑

How To Use Machine Learning For eCommerce - Aiiot Talk


The e-commerce market is vast and only expected to get bigger with time. Not only has the global e-commerce market been valued at $9.09 trillion, but it is also expected to grow at a compound annual growth rate of 14.7 per cent over the next seven years. As a result of this enormous size, companies […]

AI Research News Update: Week 1 (Nov 15-21, 2021)


The ventral visual stream is widely known for supporting the perception of faces and objects. Extracellular single neuron recordings define canonical coding principles at various stages of the processing hierarchy, such as the sensitivity of early visual neurons to orientated outlines and more anterior ventral stream neurons to complex objects and faces, over decades. A sub-network of the inferotemporal cortex dedicated to facial processing has received a lot of attention. Faces appear to be encoded in low-dimensional neural codes inside such patches, with each neuron encoding an orthogonal axis of variation in the face space. How such representations might emerge from learning from the statistics of visual input is an essential but unresolved subject. The active appearance model (AAM), the most successful computational model of face processing, is a largely handcrafted framework that can't help answer the question of finding a general learning principle that can match AAM in terms of explanatory power while having the potential to generalize beyond faces.

Giuliano Liguori on LinkedIn: #DataScience #DataScientists #DigitalTransformation


In this episode of SaugaTalks, hosted by Irene Lyakovetsky, I met Stu Bailey, Co-founder and Chief Enterprise AI Architect at ModelOp, to discuss why #ModelOps is a Critical Piece of Enterprise AI Strategy. It is not surprising seeing why #ModelOps is the cornerstone of every #AI initiative. Technology Talk Host: SaugaTalks Chats With Fascinating People In Tech! Follow: and Subscribe For The Full Episodes:

Save an extra 40% off on apps and software during this Black Friday sale


Black Friday isn't only reserved for massive TVs, kitchen appliances, and home furniture. The biggest sale of the year also applies to apps and software. If you don't want to pay full price for useful apps that can supercharge your productivity, you can take your pick from these 20 options that range from fitness programs to language learning apps to cloud storage. A subscription to Degoo nets you 10TB of supremely secured backup space for all of your videos, photos, software, and other large files -- for life. That's more space than Dropbox, OneDrive, and Google Drive combined.

Next business stress test: Content management modernisation


According to Forrester, cloud, artificial intelligence and the importance of external partners are issues that will be even more prominent as we move forward in the digital world. Businesses still maturing can learn from leaders already at the forefront of content management modernisation programmes. Forrester's research indicates that in addition to content services, leaders are more likely to embrace three areas identified as essential to future-fit content strategies: Cloud computing: A cloud foundation provides scale and adaptivity, as well as continuous vendor-delivered innovation. Cloud deployment has been growing in importance, and adoption will only increase as the pressure to serve remote workers intensifies. While the road to cloud remains varied, the Forrester survey notes that respondents predict a steep decline in their use of on-premises content deployments over the next two years, in favour of cloud-based options. While laggards are reported to most likely depend primarily on on-premises deployments today, leaders are more likely to have already made the cloud transition.

Kumar Chinnakali on LinkedIn: #AI #Machines #Humans


Serving Telco, Media & Technology sector to thrive with the strategy of customer first, enterprise transformation, intelligent industry, and with society approach. Super thanks a ton Capgemini #ArchitectsNorthAmericaCommunity for the #NAArchitectsSummit2021. Had a fantastic day one and my takeaways hold enormous, but just to name a few. Architects wear different lens perspectives to maximize the business values with the client first and client-centric approach. Be a great student for Client, Partners, & Organizations. More miles to go with #architectscommunity for now and 2022 beyond.

Deploy machine learning models on Google Cloud AI Platform


For that, you need frameworks and tooling, software and hardware that help you effectively deploy ML models. These can be frameworks like Tensorflow, Pytorch, and Scikit-Learn for training models, programming languages like Python, Java, and Go, and even cloud environments like AWS, GCP, and Azure. My Course is meant for anyone who already knows how to build both machine and deep learning models that is interested in deploying them easily on Google Cloud AI Platform. So that they can send the deployed models post requests. Also you must be familiar with Natural Language Processing and some basic cloud concepts.