Goto

Collaborating Authors

Robotics & Automation


Pinaki Laskar on LinkedIn: #AI #DeepLearning #Machinelearning

#artificialintelligence

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.


How do we know AI is ready to be in the wild? Maybe a critic is needed

#artificialintelligence

Mischief can happen when AI is let loose in the world, just like any technology. The examples of AI gone wrong are numerous, the most vivid in recent memory being the disastrously bad performance of Amazon's facial recognition technology, Rekognition, which had a propensity to erroneously match members of some ethnic groups with criminal mugshots to a disproportionate extent. Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy? "This is a really good question, and one we are actively working on," Sergey Levine, assistant professor with the University of California at Berkeley's department of electrical engineering and computer science, told ZDNet by email this week. Levine and colleagues have been working on an approach to machine learning where the decisions of a software program are subjected to a critique by another algorithm within the same program that acts adversarially.


Why Java is the Most Preferred for Artificial Intelligence - Techiexpert.com

#artificialintelligence

AI has brought digital transformation into business operations across various industries. It has become a significant part of our lifestyle. We can offer many use cases where Artificial Intelligence simplifies the process workflow, from autopilots for self-driving cars to using robots to handle warehouse jobs, implementation of chatbots in the customer care portals and more. The Artificial Intelligence technology implications for the purpose of business processes in different sectors are enormous. That is why the purpose and need for hiring skilled java developers to build AI-based apps is skyrocketing in recent years.


Jalandhar boys 'Driverless car' start-up catches people's fancy

#artificialintelligence

We all have heard about the driverless cars but do we know about one such start-up in Jalandhar? Minus-Zero is an initiative of two friends from different backgrounds. Gagandeep Reehal, a B.tech student of Thapar University came along with his long time friend Gursimran Kalra, who was the CBSE district topper of commerce in 2018 and a graduate from Shree Ram College of Commerce, Delhi University, one of the top most colleges of commerce in India. They studied together in the same school for almost 10 years and after changing streams they finally coordinated in September last year to build this project.


BrainChip Partners with MegaChips to Develop Next-Generation Edge-Based AI Solutions

#artificialintelligence

BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BCHPY) a leading provider of ultra-low power high performance artificial intelligence technology and the world's first commercial producer of neuromorphic AI chips and IP, today announced that MegaChips, a pioneer in the ASIC industry, has licensed BrainChip Akida IP to enhance and grow its technology positioning for next-generation, Edge-based AI solutions. A multibillion-dollar global fabless semiconductor company based in Japan, MegaChips provides chip solutions that fulfill various requirements, including low power consumption, cost and time to market, while achieving breakthrough functions and performance by fusing knowledge of Large Scale Integrations and applications for problems in device development. By partnering with BrainChip, MegaChips is able to quickly and easily maintain its industry innovator status by supplying solutions and applications that leverage the Akida revolutionary technology in markets such as automotive, IoT, cameras, gaming and industrial robotics. "As a trusted and loyal partner to market leaders, we deliver the technology and expertise they need to ensure products are uniquely designed for their customers and engineered for ultimate performance," said Tetsuo Hikawa, President and CEO of MegaChips. "Working with BrainChip and incorporating their Akida technology into our ASIC solutions service, we are better able to handle the development and support processes needed to design and manufacture integrated circuits and systems on chips that can take advantage of AI at the Edge."


Data Accuracy is Vital to Data Annotation Services

#artificialintelligence

There is so much buzz about artificial intelligence (AI) and machine learning today. It is no longer surprising to realize that most of the tools you use online, from your smartphones, most websites, and various devices, use AI-powered machine learning to enhance your interaction with multiple applications. Some machine learning applications include facial recognition, speech recognition, financial security, bus schedules, traffic prediction, medical services, social media, customer support, and retail. Moreover, writing tools such as Spell Check are developed using machine learning. Another excellent use of machine learning applications is predictive analytics.


Data Science: Supervised Machine Learning in Python

#artificialintelligence

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Pinaki Laskar on LinkedIn: Lubna Yusuf - La Legal

#artificialintelligence

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 is the next big thing in #AI innovation? It is what dubbed as Real Superintelligence (RSI) Platform as the CyberEngine of the Metaverse. Today's AI is the statistic ML & DL & ANNs, involving big data, statistic learning theory, optimization, data science and analytics, automated software, GPUs. After 70-years trials and errors with symbolic, statistic, narrow, general and supreme AI, there emerges a real, true, genuine AI as Machine Intelligence and Learning (MIL), or Man-Machine Superintelligence. Man-Machine MetaIntelligence Human Intelligence Artificial Intelligence Machine Learning Deep Learning Data Analytics ML [DNNs DL ML] AI [NAI AGI ASI] DA MIL Global AI Real AI Real Man-Machine Superintelligence The RSI will allow computers to effectively and sustainably interact with the world taking in all of the world's information to solve any possible problems and come up with any possible solutions.


Artificial intelligence and mobility, who's at the wheel? - Innovation Origins

#artificialintelligence

Last week, the Dutch Scientific Council for Government Policy (WRR) found that the Netherlands is not well prepared for the consequences of artificial intelligence (AI). In'Challenge AI, The New Systems Technology' (in Dutch), the council calls for regulation of technology and data, its use, and social implications. Machines will have more computing power than humans in a few decades. If devices with artificial intelligence then start to think and decide for themselves, it is to be hoped that they will observe a number of commandments. AI is also entering mobility, and the problems the WRR refers to are also at play there.


Why Data Needs to Be Labeled

#artificialintelligence

In order for a self-driving car to "see," "hear," "understand," "talk" and "think," it needs video, image, audio, text, LIDAR, and other sensor data to be correctly collected, structured, and understood by machine learning models. Breaking this down to just what a car "sees" requires annotating many images so that a model can learn and understand all the different street signs under all conditions. While speed limit signs may have the same shape, the car must also interpret the number on the sign to drive safely. A car must also be able to "understand" what a person is -- including an adult, a kid, and a baby, for example. To do this, pictures of many different people must be shown from all different angles so that it can start to say what is and is not a person.