Any technology is a double aged sword. It has Pros and cons. And while pros are imperative in governing any organization, the cons reflect the flaws of technology which is hard to ignore. Hence Artificial Intelligence is no exception for being a threat, especially for cybersecurity. Over the years, the threat regarding AI concerning cybersecurity has grown.
With the rise of computing power and advancements in algorithms, there's something amusing and interesting happening in the field of Artificial Intelligence and Machine Learning every day that it becomes difficult to keep up with the pace. Natural Language Processing is gaining new momentum as transformer models like BERT, GPT, RoBERT, XLNET and others are making it possible to create more advanced chatbots which can closely replicate a human and are easy to deploy and maintain along with huge cost savings. To be updated with the recent inventions and advancements, here's a list of top AI blogs I personally follow: Google has few of the best talents on the planet working for its Google research, Google Brain and the overall Google team. They have been successful in finding solutions to some of the most challenging computer science problems and are also the developers of ML platforms like Tensorflow. The Google research blog has all the articles, papers and other relevant content explaining what Google has been able to achieve.
When opportunity knocks, open the door: No one has taken heed of that adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the CUDA parallel computing software framework in 2006. Since then, Nvidia has been a big player in the world of high-end embedded AI applications, where teams of highly trained (and paid) engineers have used its hardware for things like autonomous vehicles. Now the company claims to be making it easy for even hobbyists to use embedded machine learning, with its US $100 Jetson Nano dev kit, which was originally launched in early 2019 and rereleased this March with several upgrades.
It was clear to the University of New South Wales (UNSW) that at the end of 2018, when it was developing its data strategy, it needed to improve the turnaround time it took to get information into the hands of decision makers. But to do that, the university had to set up a cloud-based data warehouse, which it opted to host in Microsoft Azure. The cloud-based warehouse now operates alongside the university's legacy data warehouse, which is currently hosted in Amazon Web Service's (AWS) EC2. "Our legacy data warehouse has been around for 10 to 15 years. But we started looking at what platforms can let us do everything that we do now, but also allows us to move seamlessly into new things like machine learning and AI," UNSW chief data and insights officer and senior lecture at the School of Computer Science and Engineering, Kate Carruthers said, speaking to ZDNet.
Artificial intelligence (AI) and machine learning are using predictive algorithms to determine a ... [ ] customer's lifecycle in social media One of the biggest challenges brands face today when utilizing social media is finding the right influencer to align with – and while a "wrong" choice likely won't hurt a brand in most cases, it could be more of a wasted effort. However, in a worst case scenario working with the wrong influencer can have a detrimental impact on a brand. According to a survey conducted by Salesforce Research last year, 92% of consumers surveyed report that trusting a brand makes them more likely to buy products and services. In addition, nearly a third – 32% – of consumers also said that the influencer's core values should also align with that of the consumer's. While human marketing teams can comb through the social media feeds of influencers for past posts to judge appropriateness as well as effectiveness, which can be a time consuming endeavor, and could still miss something important.
As children we believed in magic, imagined, and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime? Artificial Intelligence, aka AI, made its debut in real life and became the buzz word of the 21st century, providing us with new ideas to explore and incredible possibilities. And just as we were getting used to AI we were introduced to Futuristic Learning, Deep Learning, and another term we often confuse with AI: Machine Learning (ML).
In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.
Managing Partner and Co-Founder of Scale-Up VC, a Silicon Valley venture capital firm based in Palo Alto, California. Experts have warned against its potential misuse. It's now affecting aspects of our lives that many of us never anticipated: healthcare, education, employment and even national security. What could I be talking about? Artificial intelligence, or the "big AI," as I call it.
Artificial intelligence (AI) may play an increasingly essential role in criminal acts in the future. From a possibility of fraud to deepfakes AI-driven manipulation may cause harm as well. Neural processing engines (NLPs) can help AI take the darker side if they are deployed for all the wrong means. The infamous case of global celebrities caught in the web of deep fakes is not hidden. With non-ethical hackers gaining funds from the dark web and the underworld, the probability of deepfakes only grow larger.
Former Waymo and Uber self-driving car-whiz kid, Anthony Levandowski was sentenced last week to 18 months in federal prison for stealing trade secrets. Levandowski will also pay a $95,000 fine and $756,499.22 in restitution to Waymo. He co-founded Google's self-driving car program, now Waymo, in 2009 and served as the program's technical lead until January 2016, when he left to co-found self-driving truck start-up Otto. Seven months later Uber acquired Otto for $680M and named Levandowski the head of its self-driving car division. He was on top of the tech world. He appeared in Wired Magazine as the go-to voice in Silicon Valley for self-driving cars and LiDAR technology.