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Todays demand for real-time data analytics at the edge marks the dawn of a new era in machine learning (ML): edge intelligence. That need for time-sensitive data is, in turn, fueling a massive AI chip market, as companies look to provide ML models at the edge that have less latency and more power efficiency.

Conventional edge ML platforms consume a lot of power, limiting the operational efficiency of smart devices, which live on the edge. Thosedevices are also hardware-centric, limiting their computational capability and making them incapable of handling varying AI workloads. They leverage power-inefficient GPU- or CPU-based architectures and are also not optimized for embedded edge applications that have latency requirements.

Even though industry behemoths like Nvidia and Qualcomm offer a wide range of solutions, they mostly use a combination of GPU- or data center-based architectures and scale them to the embedded edge as opposed to creating a purpose-built solution from scratch. Also, most of these solutions are set up for larger customers, making them extremely expensive for smaller companies.

In essence, the $1 trillion global embedded-edge market is reliant on legacy technology that limits the pace of innovation.

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ML company Sima AI seeks to address these shortcomings with its machine learning-system-on-chip (MLSoC) platform that enables ML deployment and scaling at the edge. The California-based company, founded in 2018, announced today that it has begun shipping the MLSoC platform for customers, with an initial focus of helping solve computer vision challenges in smart vision, robotics, Industry 4.0, drones, autonomous vehicles, healthcare and the government sector.

The platform uses a software-hardware codesign approach that emphasizes software capabilities to create edge-ML solutions that consume minimal power and can handle varying ML workloads.

Built on 16nm technology, the MLSoCs processing system consists of computer vision processors for image pre- and post-processing, coupled with dedicated ML acceleration and high-performance application processors. Surrounding the real-time intelligent video processing are memory interfaces, communication interfaces, and system management all connected via a network-on-chip (NoC). The MLSoC features low operating power and high ML processing capacity, making it ideal as a standalone edge-based system controller, or to add an ML-offload accelerator for processors, ASICs and other devices.

The software-first approach includes carefully-defined intermediate representations (including the TVM Relay IR), along with novel compiler-optimization techniques. This software architecture enables Sima AI to support a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX, etc.) and compile over 120+ networks.

Many ML startups are focused on building only pure ML accelerators and not an SoC that has a computer-vision processor, applications processors, CODECs, and external memory interfaces that enable the MLSoC to be used as a stand-alone solution not needing to connect to a host processor. Other solutions usually lack network flexibility, performance per watt, and push-button efficiency all of which are required to make ML effortless for the embedded edge.

Sima AIs MLSoC platform differs from other existing solutions as it solves all these areas at the same time with its software-first approach.

The MLSoC platform is flexible enough to address any computer vision application, using any framework, model, network, and sensor with any resolution. Our ML compiler leverages the open-source Tensor Virtual Machine (TVM) framework as the front-end, and thus supports the industrys widest range of ML models and ML frameworks for computer vision, Krishna Rangasayee, CEO and founder of Sima AI, told VentureBeat in an email interview.

From a performance point of view, Sima AIs MLSoC platform claims to deliver 10x better performance in key figures of merit such as FPS/W and latency than alternatives.

The companys hardware architecture optimizes data movement and maximizes hardware performance by precisely scheduling all computation and data movement ahead of time, including internal and external memory to minimize wait times.

Sima AI offers APIs to generate highly optimized MLSoC code blocks that are automatically scheduled on the heterogeneous compute subsystems. The company has created a suite of specialized and generalized optimization and scheduling algorithms for the back-end compiler that automatically convert the ML network into highly optimized assembly codes that run on the machine learning-accelerator (MLA) block.

For Rangasayee, the next phase of Sima AIs growth is focused on revenue and scaling their engineering and business teams globally. As things stand, Sima AI has raised $150 million in funding from top-tier VCs such as Fidelity and Dell Technologies Capital. With the goal of transforming the embedded-edge market, the company has also announced partnerships with key industry players like TSMC, Synopsys, Arm, Allegro, GUC and Arteris.

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Machine learning at the edge: The AI chip company challenging Nvidia and Qualcomm - VentureBeat

A New Zealand startup producing its own servers is expanding into the realm of artificial intelligence, creating machine learning solutions that carry out common tasks while relieving people of repetitive, unsatisfying work. Having spotted an opportunity for the development of low-cost, high-efficiency and environmentally sustainable hardware, Kauricone has more recently pivoted in a fascinating direction: creating software that thinks about mundane problems, so we dont have to. These tasks include identifying trash for improved recycling, looking at items on roads for automated safety, pest identification and in the ultimate alleviation of a notoriously sleep-inducing task counting sheep.

Managing director, founder and tech industry veteran Mike Milne says Kauricone products include application servers, cluster servers and internet of things servers. It was in this latter category that the notion emerged of applying machine learning at the networks edge.

Having already developed low-cost-low power edge hardware, we realised there was a big opportunity for the application of smart computing in some decidedly not-so-enjoyable everyday tasks, relates Milne. After all, we had all the basic building blocks already: the hardware, the programming capability, and with good mobile network coverage, the connectivity.

Situation

Work is just another name for tasks people would rather not do themselves, or that we cannot do for ourselves. And despite living in a fabulously advanced age, there is a persistent reality of all manner of tasks which must be done every day, but which dont require a particularly high level of engagement or even intelligence.

It is these tasks for which machine learning (ML) is quite often a highly promising solution. ML collects and analyses data by applying statistical analysis, and pattern matching, to learn from past experiences. Using the trained data, it provides reliable results, and people can stop doing the boring work, says Milne.

There is in fact more to it than meets the eye (so to speak) when it comes to computer image recognition. Thats why Capcha challenges are often little more than Identify all the images containing traffic lights: because distinguishing objects is hard for bots. ML overcomes the challenge through the training mentioned by Milne: the computer is shown thousands of images and learns which are hits, and which are misses.

Potentially, there are as many use cases as you have dull but necessary tasks in the world, Milne notes. So far, weve tackled a few. Rocks on roads are dangerous, but monitoring thousands of kilometers of tarmac comes at a cost. Construction waste is extensive, bad for the environment and should be managed better. Sheep are plentiful and not always in the right paddock. And pests put New Zealands biodiversity at risk.

Solution

Tackling each of these problems, Kauricone started with its own-developed RISC IoT server hardware as the base. Running Ubuntu and programmed with Python or other open-source languages, the servers typically feature 4GB memory and 128GB solid state storage, the solar-powered edge devices consume as little as 3 watts and run indefinitely on a single solar panel. This makes for a reliable, low-cost field-ready device, says Milne.

The Rocks on Roads project made clear the challenges of simple image identification, with Kauricone eventually running a training model around the clock for 8 days, gathering 35,000 iterations of rock images, which expanded to 3,000,000 identifiable traits (bear in mind, a human identifies a rock almost instantly, perhaps faster if hurled). With this training, the machine became very good at detecting rocks on the roads.

For a new project involving construction waste, the Kauricone IoT server will maintain a vigilant watch on the types and amounts of waste going into building-site skips. Trained to identify types of waste, the resulting data will be the basis for improving waste management and recycling or redirecting certain items for more responsible disposal.

Counting sheep isnt only a method for accelerating sleep time, its also an essential task for farmers across New Zealand. Thats not all as an ML exercise, it anticipates the potential for smarter stock management, as does the related pest identification test case pursued by Kauricone. The ever-watchful camera and supporting hardware manage several tasks: identifying individual animals, numbering them, and also monitoring grass levels, essential for ovine nourishment. Tested so far on a small flock, this application is ready for scale.

Results

Milne says the small test cases pursued by Kauricone to date are just the beginning and anticipates considerable potential for ML applications across all walks of life. There is literally no end to the number of daily tasks where computer vision and ML can alleviate our workload and contribute to improved efficiency and, ultimately, a better and more sustainable planet, he notes.

The Rocks on Roads project promises improved safety with a lower human overhead, reducing or eliminating the possibility of human error. Waste management is a multifaceted problem, where the employment of personnel is rendered difficult owing to simple economics (and potentially stultifying work); New Zealands primary sector is ripe for technologically powered performance improvements which could boost already impressive productivity through automation and improved control; and pest management can help the Department of Conservation and allied parties achieve better results using fewer resources.

Its early days yet, says Milne, But the results from these exploratory projects are promising. With the connectivity of ever-expanding cellular and low-power networks like SIGFOX and LoraWan, the enabling infrastructure is increasingly available even in remote places. And purpose-built low power hardware brings computing right to the edge. Now, its just a matter of identifying opportunities and creating the applications.

For more information visit Kauricone's website.

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Kauricone: Machine learning tackles the mundane, making our lives easier - IT Brief New Zealand

They help to improve efficiency and profitability for utilities.

The utilities space is rapidly transforming today. Its shifting from the conventional and a highly-regulated environment to a tech-driven market at a fast clip. Collating data and optimizing manpower is a constant struggle. The smarter optimization of infrastructure has increased monumentally with the outbreak of the pandemic, and also the dependency on technology. There is an urgent need to balance the supply and demand for which Artificial Intelligence (AI) and Machine Learning (ML) can come into play. Data Science, aided by AI and ML, has been leading to several positive developments in the utilities space. Digitalization can increase the profitability of utilities by significant percentages by utilizing smart meters for grids, digital productivity tools and automating back-office processes. According to a study firms can increase their profitability from 20 percent to 30 percent.

Digital measures rewire organizations to do better through a fundamental reboot of how work gets done.

Customer Service and AI

According to a Gartner report, most AI investments by utilities most often go into customer service solutions. Some 86% of the utilities studied used AI in their digital marketing, towards call center support and customer application. This is testimony to the investments in AI and ML that can deliver a high ROI by improving speed and efficiency, thus enhancing customer experience. The AI thats customer-facing is a low-risk investment as customer enquiries are often repetitive such as billing enquiries, payments, new connections etc. AI can deliver tangible results for business on the customer service front.

Automatic Meters for Energy conservation

As manual entry and billing systems are not only time-consuming, but also susceptible to errors and are expensive too. The Automatic Meter Reading (AMR) System has made a breakthrough. The AMR enables large infrastructure set ups to collect data easily and also analyze the cost centers and the opportunities for improving the efficiencies of natural gas, electric, water sectors and more. It offers real-time billing information for budgeting. It has the advantage of being precise compared to manual entry. Additionally, it is able to store data at distribution points within the networks of the utility. This can be easily accessed over a network using devices like the mobile and handhelds. Energy consumption can be tracked to aid conservation and end energy theft.

Predictive Analytics Enable Smart grid options

By leveraging new-age technologies, utilities can benefit immensely. These technologies in the energy sector help in building smart power grids. The energy sector heavily relies on a complex infrastructure that can face multiple issues as a result of maintenance issues, weather conditions, failure of the system or equipment, demand surges and misallocation of resources. Overloading and congestion leads to a lot of energy being wasted. The grids produce a humongous data which help with risk mitigation when properly utilized. With the large volume of data that continuously pass over the grid, it can be challenging to collect and aggregate it. The operators could miss these insights which could lead to malfunction or outages. With the help of the ML algorithms, the insights can be obtained for smooth functioning of the grids. Automated data management can help maintain the data accurately. With the help of predictive analytics, the operators can predict grid failures before the customers are affected and also create greater customer satisfaction and mitigate any financial loss.

Efficient and Sustainable energy consumption

These allow for better allocation of energy for consumption as it would be based on demand and can save resources and help in load management and forecasting. AI can also deal with issues pertaining to vegetation by analyzing operational data or statistics. This can help to proactively deal with wildfires. Thus, it can become a sustainable and efficient system. To overcome issues pertaining to weather-related maintenance, automation helps receive signals and prioritize the areas that need attention to save money and cut down the downtime. To achieve this, the sector adopts ML capabilities as they need to be able to access automation fast and easily.

The construction sector is also a major beneficiary of the solutions. Building codes and architecture are often a humongous challenges that take a long time to meet. But, some solutions help the builders and developers test these applications seamlessly without any system interruptions. By integrating AI and ML in the data management platforms, the developers enable the data-science teams to spend enough time innovating and much less time on maintenance. With the rise in the computational power and accessibility to the Cloud, the deep learning algorithms are able to train faster while their cost is optimized. AI and ML are able to impact different aspects of business. AI can enhance the quality of human jobs by facilitating remote working. They can help in data collection and analysis and also provide actionable inputs. Data analytics platforms can throw light on the areas of inefficiency and help the providers keep costs down.

Though digital transformation might appear intimidating, its opportunities are much more than the cost and risk associated. Gradually, all utilities will undergo digital transformation as it has begun to take roots in the industrial sectors. This AI-led transformation will improve productivity, revenue gains, make networks more reliable and safe, accelerate customer acquisition, and facilitate entry into new areas of business. Globally, the digital utility market is growing at a CAGR of 11.7% for the period of 2019 to 2027. In 2018, the revenue generated globally for the digital utility market was 141.41 Bn and is expected to reach US$ 381.38 Bn by 2027 according to a study by ResearchAndMarkets.com. As the sector evolves, the advantages of AI and ML will come into play and lead to smarter grids, efficient operations and higher customer satisfaction. The companies that are in a position to take advantage of this opportunity will be ready for the future challenges that could emerge in the market.

Views expressed above are the author's own.

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Artificial intelligence and machine learning now integral to smart power solutions - Times of India

Many of todays marketing processes are powered by AI and machine learning. Discover how these technologies are shaping the future of customer experience.

By using artificial intelligence (AI) and machine learning (ML) along with analytics, brands are in a much better position to elevate customer service experiences at every touchpoint and create positive emotional connections.

This article will look at the ways that AI and ML are used by brands to improve customer service and support.

AI improves the customer service journey in several ways, including tracking conversations in real-time, providing feedback to service agents and using intelligence to monitor language, speech patterns and psychographic profiles to predict future customer needs.

This functionality can also drastically enhance the effectiveness of customer relationship management (CRM) and customer data platforms (CDP).

CRM platforms, including C2CRM, Salesforce Einstein and Zoho, have integrated AI into their software to provide real-time decisioning, predictive analysis and conversational assistants, all of which help brands more fully understand and engage their customers.

CDPs, such as Amperity, BlueConic, Adobes Real-Time CDP and ActionIQ, have also integrated AI into more traditional capabilities to unify customer data and provide real-time functionality and decisoning. This technology enables brands to gain a deeper understanding of what their customers want, how they feel and what they are most likely to do next.

Related Article: What's Next for Artificial Intelligence in Customer Experience?

Artificial intelligence and machine learning are now used for gathering and analyzing social, historical and behavioral data, which allows brands to gain a much more complete understanding of their customers.

Because AI continuously learns and improves from the data it analyzes, it can anticipate customer behavior. As such, AI- and ML-driven chatbots can provide customers with a more personalized, informed conversation that can easily answer their questions and if not, immediately route them to a live customer service agent.

Bill Schwaab, VP of sales, North America for boost.ai, told CMSWire that ML is used in combination with AI and a number of other deep learning models to support todays virtual customer service agents.

ML on its own may not be sufficient to gain a total understanding of customer requests, but its useful in classifying basic user intent, said Schwaab, who believes that the brightest applications of these technologies in customer service find the balance between AI and human intervention.

Virtual agents are becoming the first line in customer experience in addition to human agents, he explained. Because these virtual agents can resolve service queries quickly and are available outside of normal service hours, human agents can focus on more complex or valuable customer interactions. Round-the-clock availability provides brands with additional time to capture customer input and inform better decision-making.

Swapnil Jain, CEO and co-founder of Observe.AI, said that todays customer service agents no longer have to spend as much time on simpler, transactional interactions, as digital and self-serve options have reduced the volume of those tasks.

"Instead, agents must excel at higher-value, complex behaviors that meaningfully impact CX and revenue," said Jain, adding that brands are harnessing AI and ML to up-level agent skills, which include empathy and active listening. This, in turn, "drives the behavioral changes needed to improve CX performance at speed and scale."

Because customer conversations contain a goldmine of insights for improving agent performance, AI-powered conversation intelligence can help brands with everything from service and support to sales and retention, said Jain. Using advanced interaction analytics, brands can benefit from pinpointing positive and negative CX drivers, advanced tonality-based sentiment and intent analysis and evidence-based agent coaching.

Predictive analytics is the process of using statistics, data mining and modeling to make predictions.

AI can analyze large amounts of data in a very short time, and along with predictive analytics, it can produce real-time, actionable insights that can guide interactions between a customer and a brand. This practice is also referred to as predictive engagement and uses AI to inform a brand when and how to interact with each customer.

Don Kaye, CCO of Exasol, spoke with CMSWire about the ways brands are using predictive analytics as part of their data strategies that link to their overall business objectives.

Weve seen first-hand how businesses use predictive analytics to better inform their organizations decision-making processes to drive powerful customer experiences that result in brand loyalty and earn consumer trust, said Kaye.

As an example, he told CMSWire that banks use supervised learning or regression and classification to calculate the risks of loan defaults or IT departments to detect spam.

With retailers, weve seen them seeking the benefits of deep learning or reinforcement learning, which enables a new level of end-to-end automation, where models become more adaptable and use larger data volumes for increased accuracy, he said.

According to Kaye, businesses with advanced analytics also tend to have agile, open data architectures that promote open access to data, also known as data democratization.

Kaye is a big advocate for AI and ML and believes that the technologies will continue to grow and become routine across all verticals, with the democratization of analytics enabling data professionals to focus on more complex scenarios and making customer experience personalization the norm.

Related Article: What Customer-Centric Predictive Analytics Looks Like

AI-driven sentiment analysis enables brands to obtain actionable insights which facilitate a better understanding of the emotions that customers feel when they encounter pain points or friction along the customer journey as well as how they feel when they have positive, emotionally satisfying experiences.

Julien Salinas, founder and CTO at NLP Cloud, told CMSWire that AI is often used to perform sentiment analysis to automatically detect whether an incoming customer support request is urgent or not. "If the detected sentiment is negative, the ticket is more likely to be addressed quickly by the support team."

Sentiment analysis can automatically detect emotions and opinions by classifying customer text as positive, negative or neutral through the use of AI, natural language processing (NLP) and ML.

Pieter Buteneers, director of engineering in ML and AI at Sinch, said that NLP enables applications to understand, write and speak languages in a manner that is similar to humans.

"It also facilitates a deeper understanding of customer sentiment, he explained. When NLP is incorporated into chatbots and voice bots it permits them to have seemingly human-like language proficiency and adjust their tones during conversations.

When used in conjunction with chatbots, NLP can facilitate human-like conversations based on sentiment. So if a customer is upset, for example, the bot can adjust its tone to diffuse the situation while moving along the conversation, said Buteneers. This would be an intuitive shift for a human, but bots that arent equipped with NLP sentiment analysis could miss the subtle cues of human sentiment in the conversation, and risk damaging the customer relationship."

Buteneers added that breakthroughs in NLP are making an enormous difference in how AI understands input from humans. For example, NLP can be used to perform textual sentiment analysis, which can decipher the polarity of sentiments in text."

Similar to sentiment analysis, AI is also useful for detecting intent. Salinas said that its sometimes difficult to have a quick grasp on a user request, especially when the users message is very long. In that case, AI can automatically extract the main idea from the message so the support agent can act more quickly.

While AI and ML have continued to evolve, and brands have found many ways to use these technologies to improve the customer service experience, the challenges of AI and ML can still be daunting.

Kaye explained that AI models need good data to deliver accurate results, so brands must also focus on quality and governance.

In-memory analytics databases will become the driver of creation, storage and loading features in ML training tools given their analysis capabilities, and ability to scale and deliver optimal time to insight, said Kaye. He added that these tools will benefit from closer integration with the companys data stores, which will enable them to run more effectively on larger data volumes to guarantee greater system scalability.

Iliya Rybchin, partner at Elixirr Consulting, told CMSWire that thanks to ML and the vast amount of data bots are collecting, they are getting better and will continue to improve. The challenge is that they will improve in proportion to the data they receive.

Therefore, if an under-represented minority with a unique dialect is not utilizing a particular service as much as other consumers, the ML will start to discount the aspects of that dialect as outliers vs. common language, said Rybchin.

He explained that the issue is not caused by the technology or programming, but rather, it is the result of the consumer-facing product that is not providing equal access to the bot. The solution is more about bringing more consumers to the product vs. changing how the product is built or designed."

AI and ML have been incorporated into the latest generations of CDP and CRM platforms, and conversational AI-driven bots are assisting service agents and enhancing and improving the customer service experience. Predictive analytics and sentiment analysis, meanwhile, are enabling brands to obtain actionable insights that guide the subsequent interactions between a customer and a brand.

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4 Ways AI, Analytics and Machine Learning Are Improving Customer Service and Support - CMSWire

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Were in the midst of a data revolution. The volume of digital data created within the next five years will total twice the amount produced so far and unstructured data will define this new era of digital experiences.

Unstructured data information that doesnt follow conventional models or fit into structured database formats represents more than 80% of all new enterprise data. To prepare for this shift, companies are finding innovative ways to manage, analyze and maximize the use of data in everything from business analytics to artificial intelligence (AI). But decision-makers are also running into an age-old problem: How do you maintain and improve the quality of massive, unwieldy datasets?

With machine learning (ML), thats how. Advancements in ML technology now enable organizations to efficiently process unstructured data and improve quality assurance efforts. With a data revolution happening all around us, where does your company fall? Are you saddled with valuable, yet unmanageable datasets or are you using data to propel your business into the future?

Theres no disputing the value of accurate, timely and consistent data for modern enterprises its as vital as cloud computing and digital apps. Despite this reality, however, poor data quality still costs companies an average of $13 million annually.

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To navigate data issues, you may apply statistical methods to measure data shapes, which enables your data teams to track variability, weed out outliers, and reel in data drift. Statistics-based controls remain valuable to judge data quality and determine how and when you should turn to datasets before making critical decisions. While effective, this statistical approach is typically reserved for structured datasets, which lend themselves to objective, quantitative measurements.

But what about data that doesnt fit neatly into Microsoft Excel or Google Sheets, including:

When these types of unstructured data are at play, its easy for incomplete or inaccurate information to slip into models. When errors go unnoticed, data issues accumulate and wreak havoc on everything from quarterly reports to forecasting projections. A simple copy and paste approach from structured data to unstructured data isnt enough and can actually make matters much worse for your business.

The common adage, garbage in, garbage out, is highly applicable in unstructured datasets. Maybe its time to trash your current data approach.

When considering solutions for unstructured data, ML should be at the top of your list. Thats because ML can analyze massive datasets and quickly find patterns among the clutter and with the right training, ML models can learn to interpret, organize and classify unstructured data types in any number of forms.

For example, an ML model can learn to recommend rules for data profiling, cleansing and standardization making efforts more efficient and precise in industries like healthcare and insurance. Likewise, ML programs can identify and classify text data by topic or sentiment in unstructured feeds, such as those on social media or within email records.

As you improve your data quality efforts through ML, keep in mind a few key dos and donts:

Your unstructured data is a treasure trove for new opportunities and insights. Yet only 18% of organizations currently take advantage of their unstructured data and data quality is one of the top factors holding more businesses back.

As unstructured data becomes more prevalent and more pertinent to everyday business decisions and operations, ML-based quality controls provide much-needed assurance that your data is relevant, accurate, and useful. And when you arent hung up on data quality, you can focus on using data to drive your business forward.

Just think about the possibilities that arise when you get your data under control or better yet, let ML take care of the work for you.

Edgar Honing is senior solutions architect at AHEAD.

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Solve the problem of unstructured data with machine learning - VentureBeat