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EETimes - Chip Startups for AI in Edge and Endpoint Applications

#artificialintelligence

As the industry grapples with the best way to accelerate AI performance to keep up with requirements from cutting-edge neural networks, there are many startup companies springing up around the world with new ideas about how this is best achieved. This sector is attracting a lot of venture capital funding and the result is a sector rich in not just cash, but in novel ideas for computing architectures. Here at EETimes we are currently tracking around 60 AI chip startups in the US, Europe and Asia, from companies reinventing programmable logic and multi-core designs, to those developing their own entirely new architectures, to those using futuristic technologies such as neuromorphic (brain-inspired) architectures and and optical computing. Here is a snapshot of ten we think show promise, or at the very least, have some interesting ideas. We've got them categorized by where in the network their products are targeted: data centers, endpoints, or AIoT devices.


Do AI and Blockchain double the Value or double the hipe

#artificialintelligence

Artificial Intelligence (AI) has a market full of hype, with vendors, customers, and media speaking non stop about the abilities of AI on worldwide and their contributions individually. Blockchain is also generally hyped in the market, with technology providers and clients claiming all sorts of abilities that may or may not be possible. Combining AI and blockchain can obtain double the hype? On the other hand, AI is implemented real, the actual value in many endless ways we talk about every day. Likewise, blockchain is starting to show value across a variety of applications and businesses.


Deep Learning Inference at Scale

#artificialintelligence

Dashcams are an essential tool in a trucking fleet, both for the truck drivers and the fleet managers. Video footage can exonerate drivers in accidents, as well as provide opportunities for fleet managers to coach drivers. However, with a continuously running camera, there is simply too much footage to examine. When a KeepTruckin dashcam is paired with one of our Vehicle Gateways, the camera only automatically uploads the footage immediately preceding a driver performance event (DPE), which is an anomalous and potentially dangerous driver-initiated event (e.g. With all of the videos uploaded per day, fleet managers need to sift through the incoming data so that they can direct their attention to the most important videos for safety analysis. And of the selected videos for viewing, they need video overlays to more easily understand what happened in them.


A/B Testing ML models in production using Amazon SageMaker

#artificialintelligence

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thomson Reuters, use Amazon SageMaker to remove the heavy lifting from the ML process. With Amazon SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon CloudWatch, enable autoscaling to automatically scale endpoints based on traffic, and update your models in production without losing any availability. In many cases, such as e-commerce applications, offline model evaluation isn't sufficient, and you need to A/B test models in production before making the decision of updating models.


Deploy Your First Serverless AWS ML Solution Fast

#artificialintelligence

I've been working with AWS SageMaker for a while now and have enjoyed great success. Creating and tuning models, architecting pipelines to support both model development and real-time inference, and data lake formation have all been made easier in my opinion. AWS has proven to be an all encompassing solution for machine learning use cases, both batch and real-time, helping me decrease time to delivery. Prior to my exposure to public cloud services, I spent a lot of time working in hadoop distributions to deliver the processing power and storage requirements for data lake construction, and utilized Docker to provide data science sandboxes running R studio or Jupyter notebook. The install/configuration time was a turn off to a lot of clients.


Deepfakes and AI: Fighting Cybersecurity Fire with Fire

#artificialintelligence

Today, the most successful and damaging cyberattacks are executed by highly professional criminal networks rather than "lone-wolf" hackers. These criminal organizations have also become highly adept at leveraging artificial intelligence (AI) and machine learning (ML) tools, making it extremely hard for IT security organizations to keep up -- much less stay ahead of these threats. Cybercriminals are using AI and ML to exploit vulnerabilities such as user behavior or security gaps to gain access to valuable business systems and data. A perfect example of these types of threats are deepfakes – they are realistic, hard to detect and surprisingly easy-to-create facsimiles of real people. Deepfakes have been rightly denounced for the personal harm they inflict through celebrity pornographic videos, the spread of fake news, conspiracy theories, hoaxes and financial fraud.


IoT's big challenge: Managing billions of devices

#artificialintelligence

The breadth of IoT's distribution over the coming years will frustrate efforts to harness it as a unified resource. IoT application developers are embedding algorithmic capabilities in resource-constrained endpoints such as mobile phones, business machines, and consumer products of every type. More IoT edges are acquiring the ability to make decisions and take actions autonomously. More IoT devices are embedding machine learning and other sophisticated analytic algorithms. Though they're growing more powerful, IoT endpoints will continue to rely on resources located elsewhere in the sprawl, such as on adjacent devices, nearby gateways, and always-on public clouds.


Microsoft Cognitive Services at the Edge

#artificialintelligence

As AI Developers we understand the power that the Cognitive Services provide. The facility to build intelligent and supported algorithms into apps, websites, and bots to see, hear, speak, understand, and interpret your user needs. The APIs in Cognitive Services are hosted on a growing network of Microsoft-managed data centers. We need to start thinking on new scenarios that were simply not possible before. Smart sensors, connected devices are becoming a trend towards a future powered by the intelligent cloud and intelligent edge.


One-shot path planning for multi-agent systems using fully convolutional neural network

arXiv.org Machine Learning

Path planning plays a crucial role in robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated iteratively by predicting the next state based on the current state. Moreover, in case of multi-agent systems, paths are planned for each agent separately. In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. We demonstrate that our method is able to successfully generate optimal or close to optimal paths in more than 98\% of the cases for single path predictions. Moreover, we show that although the network has never been trained on multi-path planning it is also able to generate optimal or close to optimal paths in 85.7\% and 65.4\% of the cases when generating two and three paths, respectively.


Tasting Azure Machine Learning : Diabetes Prediction by Auto ML

#artificialintelligence

Few years ago, I shared first machine learning story about insurance claim prediction. It's based on python code with logistic regression algorithm to build simple classification model as demonstration purpose. In 2020, it should be the year of Automatic Machine Learning (Auto ML) to make machine learning process clean, simple, fast and everyone can taste it, even peoples haven't knowledge in machine learning or data science. Recently, due to job related, I'm helping my customer to explore/evaluate data science and machine learning platform solution. That's surprise me that Azure Machine Learning (AML) is enhanced a lot and really provided an end-to-end solution platform and take care wide ranges of end users, from newbie to expert.