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knowledge connexions

Artificial intelligence in Healthcare

Artificial intelligence (ai) is improving healthcare by reducing errors and saving lives. AI was valued at $600 million in 2014 and is projected to reach $150 billion by 2026.

AI applications in healthcare include finding new links between genetic codes or to drive surgery-assisting robots.

How does AI help healthcare?

David B Agus, MD, professor of Medicine and Engineering at the university of Southern California believes Artificial Intelligence (AI) is already here and it’s fundamentally changing medicine. Machine learning allows computers to “learn with incoming data and identify patterns and make decisions with minimal human direction,” he added.

Which is the best application of AI in the healthcare?

Pathai is developing machine learning technology to assist pathologists in making more accurate diagnoses. The company has worked with drug developers like Bristol-Myers Squibb and organizations like the Bill Melinda Gates Foundation to expand its AI technology into other healthcare industries.

Categories
knowledge connexions

Artificial intelligence in Healthcare

Artificial intelligence (ai) is improving healthcare by reducing errors and saving lives. AI was valued at $600 million in 2014 and is projected to reach $150 billion by 2026.

AI applications in healthcare include finding new links between genetic codes or to drive surgery-assisting robots.

How does AI help healthcare?

David B Agus, MD, professor of Medicine and Engineering at the university of Southern California believes Artificial Intelligence (AI) is already here and it’s fundamentally changing medicine. Machine learning allows computers to “learn with incoming data and identify patterns and make decisions with minimal human direction,” he added.

Which is the best application of AI in the healthcare?

Pathai is developing machine learning technology to assist pathologists in making more accurate diagnoses. The company has worked with drug developers like Bristol-Myers Squibb and organizations like the Bill Melinda Gates Foundation to expand its AI technology into other healthcare industries.

Categories
knowledge connexions

Run:AI takes your AI and runs it, on the super-fast software stack of the future

Startup Run:AI exits stealth, promises a software layer to abstract over many AI chips

It’s no secret that machine learning in its various forms, most prominently deep learning, is taking the world by storm. Some side effects of this include the proliferation of software libraries for training machine learning algorithms, as well as specialized AI chips to run those demanding workloads.

The time and cost of training new models are the biggest barriers to creating new AI solutions and bringing them quickly to market. Experimentation is needed to produce good models, and slightly-modified training workloads could be run hundreds of times before they’re accurate enough to use. This results in very long times-to-delivery, as workflow complexities and costs grow.

Today Tel Aviv startup Run:AI exits stealth mode, with the announcement of $13 million in funding for what sounds like an unorthodox solution: rather than offering another AI chip, Run:AI offers a software layer to speed up machine learning workload execution, on premise and in the cloud.

The company works closely with AWS, and is a VMware technology partner. Its core value proposition is to act as a management platform to bridge the gap between the different AI workloads and the various hardware chips, and run a really efficient and fast AI computing platform.

AI chip virtualization

When we first heard about it, we were skeptical. A software layer that sits on top of hardware sounds a lot like virtualization. Is virtualization really a good idea when it’s all about being as close to the metal as possible to squeeze as much performance out of AI chips as possible? This is what Omri Geller, Run:AI co-founder and CEO thinks:

“Traditional computing uses virtualization to help many users or processes share one physical resource efficiently; virtualization tries to be generous. But a deep learning workload is essentially selfish since it requires the opposite:

It needs the full computing power of multiple physical resources for a single workload, without holding anything back. Traditional computing software just can’t satisfy the resource requirements for deep learning workloads.”

abstraction-layer-runai.png

Run:AI works as an abstraction layer on top of hardware running AI workloads

So, even though this sounds like virtualization, it’s a different kind of virtualization. Run:AI claims to have completely rebuilt the software stack for deep learning to get past the limits of traditional computing, making training massively faster, cheaper and more efficient.

Still, AI chip manufacturers have their own software stacks, too. Presumably, they know their own hardware better. Why would someone choose to use a 3rd party software layer like Run:AI? And what AI chips does Run:AI support?

Geller noted that there is diversity in hardware for AI that is currently available and will become available in the next few years. Currently in production the Run:AI platform supports Nvidia GPUs, while Geller said that Google’s TPUs will be supported in next releases. He went on to add that other deep learning dedicated chips will be supported as well once they are ready and available for general use. But that’s not all.

Machine learning workload diversity and the need for a management platform

Geller pointed out that in the new era of AI, diversity comes not only in the various available hardware chips but also in the workloads themselves. AI workloads include Support-Vectors, decision tree algorithms, fully connected neural networks, Convolutional Neural Networks (CNNs), long-short-term memory (LSTM) and others:

“Each algorithm fits a different application (decision trees for recommendation engines, CNNs for image recognition, LSTMs for NLP, and so on). These workloads need to run with different optimizations – different in terms of distribution strategy, on different hardware chips, etc.

A management platform is required to bridge the gap between the different AI workloads and the various hardware chips and run a real efficient and fast AI computing platform. Run:AI’s system runs all an organization’s AI workloads concurrently, and therefore can apply macro-optimizations like allocating resources among the various workloads”.

The state of AI in 2020: Biology and healthcare’s AI moment, ethics, predictions, and graph neural networks

Run:AI uses graph analysis coupled with a unique hardware modeling approach to handle deep learning optimizations and manage a large set of workloads

Geller explained that Run:AI uses graph analysis coupled with a unique hardware modeling approach to handle such optimizations and manage a large set of workloads. This, he said, allows the platform to understand the computational complexity of the workloads, matching the best hardware configuration to each task while taking into account business goals and pre-defined cost and speed policies.

Geller added that Run:AI also automatically distributes computations over multiple compute resources using hybrid data/model parallelism, treating many separate compute resources as though they are a single computer with numerous compute nodes that work in parallel. This approach optimizes compute efficiency and allows you to increase the size of the trainable neural network.

Running machine learning model training workloads, however, is heavily reliant on feeding them with the data they need. In addition, people usually develop their models using TensorFlow, Keras, PyTorch, or one of the many machine learning frameworks around.

So how does this all come together – what do machine learning engineers have to do to run their model on Run:AI, and feed it the data it needs? Importantly, does it also work in the cloud – public and private? Many AI workloads run in the cloud, following data gravity.

Integrating with machine learning frameworks and data storage, on premise and in the cloud

Geller said that one of the core concepts of Run:AI is that the user doesn’t have to change workflows in order to use the system:

“Run:AI supports both private clouds and public clouds such that our solution works in hybrid/multi cloud environments. The company works closely with VMware (technology partner) and with AWS in order to maximize resource utilization and minimize costs.

Run:AI can operate with Docker containers pre-built by the user, containers pre-built by the Run:AI team, or on bare metal. Most of Run:AI optimizations can be applied to any containerized workload running with any framework. The low-level system that parallelizes a single workload to run on multiple resources can be applied to graph-based frameworks, currently supporting TensorFlow and Keras in production and soon PyTorch as well.

Data is streamed to the compute instance either via containerized entry point scripts, or as part of the training code running on bare metal hardware. Data can be stored in any location including cloud storage in public clouds and network file systems in private clouds”.

Again, this made us wonder. As Run:AI claims to work close to the metal, it seemed to us like a different model, conceptually, from the cloud where the idea is to abstract from the hardware, and use a set of distributed nodes for compute and storage. Plus, one of the issues with Docker / Kubernetes at this time is that (permanent & resilient) data storage is complicated.

In most cases, Geller said, data is stored in a cloud storage like AWS S3 and pipelined to the compute instance:

“The data pipeline typically includes a phase of streaming the data from the cloud storage to the compute instance and a preprocessing phase of preparing the data to be fed to the neural net trainer. Performance degradation can occur in any of these phases.

The Run:AI system accounts for data gravity and optimizes the data streaming performance by making sure the compute instance is as near as possible to the data storage. The low-level features of the Run:AI system further analyze the performance of the data pipeline, alerting users on bottlenecks either in the data streaming phase or in the preprocessing step while providing recommendations for improvement”.

Geller added that there is also an option for an advanced users to tweak the results of the Run:AI layer, manually determining the amount of resources and the distribution technique, and the workload would be executed accordingly.

Does Run:AI have legs?

Run:AI’s core value proposition seems to be acting as the management layer above AI chips. Run:AI makes sense as a way of managing workloads efficiently across diverse infrastructure. In a way, Run:AI can help cloud providers and data center operators hedge their bets: rather than putting all their eggs in one AI chip vendor basket, they can have a collection of different chips, and use Run:AI as the management layer to direct workloads where they are most suitable for.

Promising as this may sound, however, it may not be everyone’s cup of tea. If your infrastructure is homogeneous, consisting of a single AI chip, it’s questionable whether Run:AI could deliver superior performance than the chip’s own native stack. We asked whether there are any benchmarks: could Run:AI’s performance be faster than Nvidia, GraphCore, or Habana, for example? It seems at this point there are no benchmarks that can be shared.

omri-geller-and-dr-ronen-dar.jpg

Run:AI founders, Omri Geller and Dr. Ronen Dar. Raun:AI is in private beta with paying customers and working with AWS and VMware. General availability is expected in Q4 2019

Geller, who co-founded Run:AI with Dr. Ronen Dar and Prof. Meir Feder in 2018, said that there are currently several paying customers from the retail, medical, and finance verticals. These customers use Run:AI to speed up their training and simplify their infrastructure.

He went on to add that customers also use the system as an enabler to train big models that they couldn’t train before because the model doesn’t fit into a single GPU memory: “Our parallelization techniques can bypass these limits. Customers are able to improve their model accuracy when accelerating their training processes and training bigger models”.

Run:AI’s business model is based on subscription and the parameters are a combination of the number of users and the number of experiments. The cost depends on the size and volume of the company, Geller said. Currently Run:AI is in private beta, with general availability expected in 6 months.

Content retrieved from: https://www.zdnet.com/article/take-your-ai-and-run-it-on-the-super-fast-software-stack-of-the-future/.

Categories
knowledge connexions

Run:AI takes your AI and runs it, on the super-fast software stack of the future

Startup Run:AI exits stealth, promises a software layer to abstract over many AI chips

It’s no secret that machine learning in its various forms, most prominently deep learning, is taking the world by storm. Some side effects of this include the proliferation of software libraries for training machine learning algorithms, as well as specialized AI chips to run those demanding workloads.

The time and cost of training new models are the biggest barriers to creating new AI solutions and bringing them quickly to market. Experimentation is needed to produce good models, and slightly-modified training workloads could be run hundreds of times before they’re accurate enough to use. This results in very long times-to-delivery, as workflow complexities and costs grow.

Today Tel Aviv startup Run:AI exits stealth mode, with the announcement of $13 million in funding for what sounds like an unorthodox solution: rather than offering another AI chip, Run:AI offers a software layer to speed up machine learning workload execution, on premise and in the cloud.

The company works closely with AWS, and is a VMware technology partner. Its core value proposition is to act as a management platform to bridge the gap between the different AI workloads and the various hardware chips, and run a really efficient and fast AI computing platform.

AI chip virtualization

When we first heard about it, we were skeptical. A software layer that sits on top of hardware sounds a lot like virtualization. Is virtualization really a good idea when it’s all about being as close to the metal as possible to squeeze as much performance out of AI chips as possible? This is what Omri Geller, Run:AI co-founder and CEO thinks:

“Traditional computing uses virtualization to help many users or processes share one physical resource efficiently; virtualization tries to be generous. But a deep learning workload is essentially selfish since it requires the opposite:

It needs the full computing power of multiple physical resources for a single workload, without holding anything back. Traditional computing software just can’t satisfy the resource requirements for deep learning workloads.”

abstraction-layer-runai.png

Run:AI works as an abstraction layer on top of hardware running AI workloads

So, even though this sounds like virtualization, it’s a different kind of virtualization. Run:AI claims to have completely rebuilt the software stack for deep learning to get past the limits of traditional computing, making training massively faster, cheaper and more efficient.

Still, AI chip manufacturers have their own software stacks, too. Presumably, they know their own hardware better. Why would someone choose to use a 3rd party software layer like Run:AI? And what AI chips does Run:AI support?

Geller noted that there is diversity in hardware for AI that is currently available and will become available in the next few years. Currently in production the Run:AI platform supports Nvidia GPUs, while Geller said that Google’s TPUs will be supported in next releases. He went on to add that other deep learning dedicated chips will be supported as well once they are ready and available for general use. But that’s not all.

Machine learning workload diversity and the need for a management platform

Geller pointed out that in the new era of AI, diversity comes not only in the various available hardware chips but also in the workloads themselves. AI workloads include Support-Vectors, decision tree algorithms, fully connected neural networks, Convolutional Neural Networks (CNNs), long-short-term memory (LSTM) and others:

“Each algorithm fits a different application (decision trees for recommendation engines, CNNs for image recognition, LSTMs for NLP, and so on). These workloads need to run with different optimizations – different in terms of distribution strategy, on different hardware chips, etc.

A management platform is required to bridge the gap between the different AI workloads and the various hardware chips and run a real efficient and fast AI computing platform. Run:AI’s system runs all an organization’s AI workloads concurrently, and therefore can apply macro-optimizations like allocating resources among the various workloads”.

The state of AI in 2020: Biology and healthcare’s AI moment, ethics, predictions, and graph neural networks

Run:AI uses graph analysis coupled with a unique hardware modeling approach to handle deep learning optimizations and manage a large set of workloads

Geller explained that Run:AI uses graph analysis coupled with a unique hardware modeling approach to handle such optimizations and manage a large set of workloads. This, he said, allows the platform to understand the computational complexity of the workloads, matching the best hardware configuration to each task while taking into account business goals and pre-defined cost and speed policies.

Geller added that Run:AI also automatically distributes computations over multiple compute resources using hybrid data/model parallelism, treating many separate compute resources as though they are a single computer with numerous compute nodes that work in parallel. This approach optimizes compute efficiency and allows you to increase the size of the trainable neural network.

Running machine learning model training workloads, however, is heavily reliant on feeding them with the data they need. In addition, people usually develop their models using TensorFlow, Keras, PyTorch, or one of the many machine learning frameworks around.

So how does this all come together – what do machine learning engineers have to do to run their model on Run:AI, and feed it the data it needs? Importantly, does it also work in the cloud – public and private? Many AI workloads run in the cloud, following data gravity.

Integrating with machine learning frameworks and data storage, on premise and in the cloud

Geller said that one of the core concepts of Run:AI is that the user doesn’t have to change workflows in order to use the system:

“Run:AI supports both private clouds and public clouds such that our solution works in hybrid/multi cloud environments. The company works closely with VMware (technology partner) and with AWS in order to maximize resource utilization and minimize costs.

Run:AI can operate with Docker containers pre-built by the user, containers pre-built by the Run:AI team, or on bare metal. Most of Run:AI optimizations can be applied to any containerized workload running with any framework. The low-level system that parallelizes a single workload to run on multiple resources can be applied to graph-based frameworks, currently supporting TensorFlow and Keras in production and soon PyTorch as well.

Data is streamed to the compute instance either via containerized entry point scripts, or as part of the training code running on bare metal hardware. Data can be stored in any location including cloud storage in public clouds and network file systems in private clouds”.

Again, this made us wonder. As Run:AI claims to work close to the metal, it seemed to us like a different model, conceptually, from the cloud where the idea is to abstract from the hardware, and use a set of distributed nodes for compute and storage. Plus, one of the issues with Docker / Kubernetes at this time is that (permanent & resilient) data storage is complicated.

In most cases, Geller said, data is stored in a cloud storage like AWS S3 and pipelined to the compute instance:

“The data pipeline typically includes a phase of streaming the data from the cloud storage to the compute instance and a preprocessing phase of preparing the data to be fed to the neural net trainer. Performance degradation can occur in any of these phases.

The Run:AI system accounts for data gravity and optimizes the data streaming performance by making sure the compute instance is as near as possible to the data storage. The low-level features of the Run:AI system further analyze the performance of the data pipeline, alerting users on bottlenecks either in the data streaming phase or in the preprocessing step while providing recommendations for improvement”.

Geller added that there is also an option for an advanced users to tweak the results of the Run:AI layer, manually determining the amount of resources and the distribution technique, and the workload would be executed accordingly.

Does Run:AI have legs?

Run:AI’s core value proposition seems to be acting as the management layer above AI chips. Run:AI makes sense as a way of managing workloads efficiently across diverse infrastructure. In a way, Run:AI can help cloud providers and data center operators hedge their bets: rather than putting all their eggs in one AI chip vendor basket, they can have a collection of different chips, and use Run:AI as the management layer to direct workloads where they are most suitable for.

Promising as this may sound, however, it may not be everyone’s cup of tea. If your infrastructure is homogeneous, consisting of a single AI chip, it’s questionable whether Run:AI could deliver superior performance than the chip’s own native stack. We asked whether there are any benchmarks: could Run:AI’s performance be faster than Nvidia, GraphCore, or Habana, for example? It seems at this point there are no benchmarks that can be shared.

omri-geller-and-dr-ronen-dar.jpg

Run:AI founders, Omri Geller and Dr. Ronen Dar. Raun:AI is in private beta with paying customers and working with AWS and VMware. General availability is expected in Q4 2019

Geller, who co-founded Run:AI with Dr. Ronen Dar and Prof. Meir Feder in 2018, said that there are currently several paying customers from the retail, medical, and finance verticals. These customers use Run:AI to speed up their training and simplify their infrastructure.

He went on to add that customers also use the system as an enabler to train big models that they couldn’t train before because the model doesn’t fit into a single GPU memory: “Our parallelization techniques can bypass these limits. Customers are able to improve their model accuracy when accelerating their training processes and training bigger models”.

Run:AI’s business model is based on subscription and the parameters are a combination of the number of users and the number of experiments. The cost depends on the size and volume of the company, Geller said. Currently Run:AI is in private beta, with general availability expected in 6 months.

Content retrieved from: https://www.zdnet.com/article/take-your-ai-and-run-it-on-the-super-fast-software-stack-of-the-future/.

Categories
knowledge connexions

Graph analytics for the people: No code data migration, visual querying, and free COVID-19 analytics by TigerGraph

Graph databases and analytics are getting ever more accessible and relevant

As we’ve been keeping track of the graph scene for a while now, a couple of things have started becoming apparent. One, graph is here to stay. Two, there’s still some way to go to make the benefits of graph databases and analytics widely available and accessible. Add to this a newly-found timeliness, as leveraging connections is where this technology shines, and you have the backdrop for today’s announcement by TigerGraph.

Graph is here to stay

Even though graph databases have a history that goes back at least 20 years, it’s only the last couple of years that it started getting in the limelight. The realization that the way data points are connected can bring more insights, and value, than sheer data volume seems to have hit home. At the same time, graph technology has been making progress, while the limitations of incumbent relational databases when it comes to leveraging connections are now well understood.

This has lead to a perfect storm for graph databases. Graph databases went from a niche market to the fastest-growing segment in data management in almost no time. Gartner, for example, predicted last year that this space will see compound annual growth of 100% on a year to year basis till 2022. Every single industry executive we’ve spoken to seems to verify this — 2019 has been a very good year indeed.

TigerGraph is no exception. TigerGraph is a relative newcomer in this space, having emerged from stealth in 2017. Before that, however, TigerGraph’s people have been working on their platform since 2012. This is starting to pay off, according to TigerGraph VP Marketing Gaurav Deshpande.

Business woman drawing global structure networking and data exchanges customer connection on dark background

Leveraging connections is where graph databases shine

TigerGraph was one of the first graph database vendors to announce a fully managed cloud service in late 2019. In a call with ZDNet, Deshpande noted that even though the cloud-based version of the platform has only been generally available for a while, it is seeing rapid uptake.

During the past four months alone, TigerGraph notes, more than 1,000 developers have harnessed the power of graph to build applications on top of TigerGraph Cloud, the company’s graph database-as-a-service. This seems to be in line with the overall trend — data, databases, and users, are all going cloud.

Still, this is just one of the pieces of the puzzle graph database vendors will need to solve. Being on offer in the cloud may take care of the availability part, but what about accessibility? Not everyone is an expert in graph to boot with. Even for the ones who are, having some kind of equivalent for the well-established technology stack that comes with incumbent relational databases would help.

Wide availability and accessibility: Cloud, no code, visual tools

This is where TigerGraph’s announcement comes into play. The first part of what TigerGraph dubs version 3.0 of its platform does not seem particularly revolutionary, but we get the feeling it will be appreciated by many: the capability to automatically migrate data from relational databases to TigerGraph, without the need to build a data pipeline or create and map to a new graph schema.

As seen in a demo released by TigerGraph, the migration seems pretty painless indeed. Deshpande commented that this was a feature TigerGraph has been working on for a while, and now the time finally came to release it. Initial customer feedback has been pretty positive, too.

Although TigerGraph is not the only graph database vendor to offer some way of importing data, other options often require an intermediate step, i.e. exporting to CSV format. This adds complexity and cost to the process, as opposed to what seems like a pretty smooth import process for TigerGraph 3.0.

The flip side of this, however, is a lack of transparency and control. At this point, there is no way for users to control the process. This means that built-in rules for mapping and schema creation apply. This may be more of a problem than it seems, especially for complex domains.

The clarity in perception and navigation, as well as performance in querying, are very much dependent on an appropriate graph data model. Depending on your domain, an out-of-the-box graph data model may or may not be appropriate. Of course, it’s a start. As Deshpande pointed out, users can always intervene to fine tune their graph data model using TigerGraph’s visual IDE.

Over time, Deshpande said, the ability to control the process will be added. For the time being, however, users need to be aware of this and be ready to intervene as needed. But that’s not all they may want to use TigerGraph’s visual IDE for. Overall, visual environments are a great boost for developer accessibility and productivity, and graph database vendors have been adding those to their arsenals, too.

TigerGraph 3.0, however, goes one step further. In an industry first, to the best of our knowledge, TigerGraph 3.0 introduces visual querying capabilities for its IDE. In other words: users can now explore their graphs, and formulate and execute queries against the database, without actually learning TigerGraph’s query language or writing code.

This patent=pending capability will probably attract some attention and goes some way into mitigating one of the issues with graph databases. While efforts to produce a universally standardized graph query language are underway, no code querying is an interesting capability in its own right.

Leveraging connections in COVID-19 times

TigerGraph 3.0 introduces more improvements, namely support for distributed environments in its cloud, and user-defined indexing. The former means that graph deployments around the globe can now scale up in a better way, while the latter means that users can speed up the database performance for specific queries.

Last but not least, however, is an initiative that comes at a time when graph analytics could really help society at large. As the spread of the COVID-19 virus has reached a pandemic status, according to the WHO, one of the key aspects of tackling the virus is identifying contacts for every individual who has been tested positive.

This essentially comes down to leveraging connections, as the name of the game is to identify people with whom COVID-19 positive cases have been in touch. The idea is to pinpoint potential upstream sources the virus may have been acquired from while keeping an eye on potential downstream contacts to try and contain further contamination.

This is exactly the type of analytics where graph shines. Mastercard, Bill & Melinda Gates Foundation and Wellcome have launched an initiative to speed development and access to therapies for COVID-19. TigerGraph took note and would like to lend a helping hand for this and all other initiatives aimed at stopping the spread of and improving treatment for coronavirus worldwide.

For this reason, TigerGraph is offering free Cloud and Enterprise Edition use for applications requiring massive data or high computation needs. Local, State and Federal agencies, corporates as well as non-profit can immediately utilize the free tier on TigerGraph Cloud to load data and perform advanced analysis.

Graph algorithms may be of help there. For example, Community Detection can identify clusters of virus infection, PageRank can identify super-spreading events, and Shortest Path may help understand the origin and impact of spread in a particular area or community.

TigerGraph’s own founding team has roots in China, and some of its executives nearly escaped being stranded in Europe due to the recently imposed travel ban. Perhaps this served as motivation for TigerGraph, but in any case, at times like these, everyone should chip in as much as they can.

Content retrieved from: https://www.zdnet.com/article/graph-analytics-for-the-people-no-code-data-migration-visual-querying-and-free-covid-19-analytics-by-tigergraph/.

Categories
knowledge connexions

Graph analytics for the people: No code data migration, visual querying, and free COVID-19 analytics by TigerGraph

Graph databases and analytics are getting ever more accessible and relevant

As we’ve been keeping track of the graph scene for a while now, a couple of things have started becoming apparent. One, graph is here to stay. Two, there’s still some way to go to make the benefits of graph databases and analytics widely available and accessible. Add to this a newly-found timeliness, as leveraging connections is where this technology shines, and you have the backdrop for today’s announcement by TigerGraph.

Graph is here to stay

Even though graph databases have a history that goes back at least 20 years, it’s only the last couple of years that it started getting in the limelight. The realization that the way data points are connected can bring more insights, and value, than sheer data volume seems to have hit home. At the same time, graph technology has been making progress, while the limitations of incumbent relational databases when it comes to leveraging connections are now well understood.

This has lead to a perfect storm for graph databases. Graph databases went from a niche market to the fastest-growing segment in data management in almost no time. Gartner, for example, predicted last year that this space will see compound annual growth of 100% on a year to year basis till 2022. Every single industry executive we’ve spoken to seems to verify this — 2019 has been a very good year indeed.

TigerGraph is no exception. TigerGraph is a relative newcomer in this space, having emerged from stealth in 2017. Before that, however, TigerGraph’s people have been working on their platform since 2012. This is starting to pay off, according to TigerGraph VP Marketing Gaurav Deshpande.

Business woman drawing global structure networking and data exchanges customer connection on dark background

Leveraging connections is where graph databases shine

TigerGraph was one of the first graph database vendors to announce a fully managed cloud service in late 2019. In a call with ZDNet, Deshpande noted that even though the cloud-based version of the platform has only been generally available for a while, it is seeing rapid uptake.

During the past four months alone, TigerGraph notes, more than 1,000 developers have harnessed the power of graph to build applications on top of TigerGraph Cloud, the company’s graph database-as-a-service. This seems to be in line with the overall trend — data, databases, and users, are all going cloud.

Still, this is just one of the pieces of the puzzle graph database vendors will need to solve. Being on offer in the cloud may take care of the availability part, but what about accessibility? Not everyone is an expert in graph to boot with. Even for the ones who are, having some kind of equivalent for the well-established technology stack that comes with incumbent relational databases would help.

Wide availability and accessibility: Cloud, no code, visual tools

This is where TigerGraph’s announcement comes into play. The first part of what TigerGraph dubs version 3.0 of its platform does not seem particularly revolutionary, but we get the feeling it will be appreciated by many: the capability to automatically migrate data from relational databases to TigerGraph, without the need to build a data pipeline or create and map to a new graph schema.

As seen in a demo released by TigerGraph, the migration seems pretty painless indeed. Deshpande commented that this was a feature TigerGraph has been working on for a while, and now the time finally came to release it. Initial customer feedback has been pretty positive, too.

Although TigerGraph is not the only graph database vendor to offer some way of importing data, other options often require an intermediate step, i.e. exporting to CSV format. This adds complexity and cost to the process, as opposed to what seems like a pretty smooth import process for TigerGraph 3.0.

The flip side of this, however, is a lack of transparency and control. At this point, there is no way for users to control the process. This means that built-in rules for mapping and schema creation apply. This may be more of a problem than it seems, especially for complex domains.

The clarity in perception and navigation, as well as performance in querying, are very much dependent on an appropriate graph data model. Depending on your domain, an out-of-the-box graph data model may or may not be appropriate. Of course, it’s a start. As Deshpande pointed out, users can always intervene to fine tune their graph data model using TigerGraph’s visual IDE.

Over time, Deshpande said, the ability to control the process will be added. For the time being, however, users need to be aware of this and be ready to intervene as needed. But that’s not all they may want to use TigerGraph’s visual IDE for. Overall, visual environments are a great boost for developer accessibility and productivity, and graph database vendors have been adding those to their arsenals, too.

TigerGraph 3.0, however, goes one step further. In an industry first, to the best of our knowledge, TigerGraph 3.0 introduces visual querying capabilities for its IDE. In other words: users can now explore their graphs, and formulate and execute queries against the database, without actually learning TigerGraph’s query language or writing code.

This patent=pending capability will probably attract some attention and goes some way into mitigating one of the issues with graph databases. While efforts to produce a universally standardized graph query language are underway, no code querying is an interesting capability in its own right.

Leveraging connections in COVID-19 times

TigerGraph 3.0 introduces more improvements, namely support for distributed environments in its cloud, and user-defined indexing. The former means that graph deployments around the globe can now scale up in a better way, while the latter means that users can speed up the database performance for specific queries.

Last but not least, however, is an initiative that comes at a time when graph analytics could really help society at large. As the spread of the COVID-19 virus has reached a pandemic status, according to the WHO, one of the key aspects of tackling the virus is identifying contacts for every individual who has been tested positive.

This essentially comes down to leveraging connections, as the name of the game is to identify people with whom COVID-19 positive cases have been in touch. The idea is to pinpoint potential upstream sources the virus may have been acquired from while keeping an eye on potential downstream contacts to try and contain further contamination.

This is exactly the type of analytics where graph shines. Mastercard, Bill & Melinda Gates Foundation and Wellcome have launched an initiative to speed development and access to therapies for COVID-19. TigerGraph took note and would like to lend a helping hand for this and all other initiatives aimed at stopping the spread of and improving treatment for coronavirus worldwide.

For this reason, TigerGraph is offering free Cloud and Enterprise Edition use for applications requiring massive data or high computation needs. Local, State and Federal agencies, corporates as well as non-profit can immediately utilize the free tier on TigerGraph Cloud to load data and perform advanced analysis.

Graph algorithms may be of help there. For example, Community Detection can identify clusters of virus infection, PageRank can identify super-spreading events, and Shortest Path may help understand the origin and impact of spread in a particular area or community.

TigerGraph’s own founding team has roots in China, and some of its executives nearly escaped being stranded in Europe due to the recently imposed travel ban. Perhaps this served as motivation for TigerGraph, but in any case, at times like these, everyone should chip in as much as they can.

Content retrieved from: https://www.zdnet.com/article/graph-analytics-for-the-people-no-code-data-migration-visual-querying-and-free-covid-19-analytics-by-tigergraph/.

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AI applications, chips, deep tech, and geopolitics in 2019: The stakes have never been higher

The state of AI in 2019 report analysis with report author, AI expert, and venture capitalist Nathan Benaich continues. High-profile applications, funding, and the politics of AI

It’s the time of the season for AI reports. As we noted earlier, the last few days saw the publication of not 1, but three top-notch reports on the state of AI. People working in VCs have authored all of them, and keeping a close eye on all things AI: From technological breakthroughs to implications in the economy and society at large.

Having covered key technological breakthroughs already, we extend the discussion on the implications of AI with Nathan Benaich, co-author of the State of AI Report 2019, Air Street Capital and RAAIS founder. Benaich co-authored the report with AI angel investor and UCL IIPP visiting professor Ian Hogarth.

Benaich and Hogarth have also drawn on the expertise of prominent figures such as Google AI Researcher and the lead of Keras Deep Learning framework François Chollet, VC and AI thought leader Kai-Fu Lee, and Facebook AI Researcher Sebastian Riedel.

AI applications: RPA and autonomous vehicles

Much of the Q&A with Benaich focused on the geopolitics of AI. That’s not to say Benaich and Hogarth’s report does not cover topics such as talent, infrastructure, or applications — it does, extensively. But with such a full plate, one has to pick.

As far as talent is concerned, there is a consensus among experts: AI talent is highly sought after (and rewarded) and investment in training is on the rise. Nonetheless, the talent shortage in AI continues to be a major bottleneck to the broad adoption of the technology across the industry.

One approach to mitigate this is AutoML, that is to say using machine learning to automate an increasing part of the process of applying machine learning, in a sort of recursive fashion. In the report, AutoML is shown to de-novo design neural networks that are better than those designed by humans to run on resource-constrained mobile devices, for example.

The macro picture remains hot. Funds invested in AI grew by almost 80 percent in 2018 compared to 2017, exceeding $27 billion per year, with North America leading the way at 55 percent market share. Some of the application areas this capital has been pouring into as emphasized in the report are robotics (mainly in manufacturing and logistics), RPA (Robotic Process Automation), healthcare, demand forecasting, autonomous vehicles, and text analysis.

RPA, which is not related to robotics, is “an overnight enterprise success, 15 years in the making”, as the report states. Benaich noted that industry adoption of RPA appears to be growing at a clip, mostly as a result of the benefits it delivers to enterprises: Reduced operating costs and increased operational nimbleness to compete with new entrants.

RPA companies saw massive funding rounds: UiPath raised $800M across two rounds in 2018 and one round in 2019, while Automation Anywhere raised $550 million across two rounds in 2018. As mentioned in FirstMark’s report, however, there are reasons to be cynical about RPA: “RPA, at this stage at least, is more about automation than intelligence, more about rules-based solutions than AI.” Benaich agrees.

Another high-profile area of application is autonomous vehicles (AV). As Benaich and Hogarth note, self-driving cars are now a game for multi-billion-dollar balance sheets. They list spending by the likes of Waymo, Uber, Cruise, and Ford to make their case. But despite growth in investment and live AV pilots in California and elsewhere, some players have missed launch dates, while others remain silent.

Benaich and Hogarth point out that while the average Californian drives 14,435 miles per year, only 11/63 companies had driven more than this in 2018. Waymo drove more than one million miles in 2018, nearly three times as much as second best GM Cruise and 16-times as much as third best Apple. As for Tesla — it does not report its disengagement metrics to the California DMV.

Allegedly, however, Tesla has more data than any of the other players, giving it a leg up in the race. Tesla also designs its own AI chip to power the compute needed on board. This is another red hot area for innovation, as it is driving the capabilities of AI. We have covered some of the pioneers in this space, such as Graphcore, Habana, and GreenWaves.

AI chips, deep tech, geopolitics: China’s rapid growth

Benaich believes the timing is right to develop novel chips that are purpose-built for training and inference of AI models:

“We think this is true because of industry adoption of AI models for several large-scale use cases, especially in consumer internet. As a result, chip designers have a clear customer to design for. Designing chips, however, is an endeavor that is very capital intensive and requires significant domain experience that can only be acquired over many many years.”

This is also closely linked to geopolitics, as per Benaich’s reasoning. Companies building this kind of “deep” or “core” sector-agnostic technology comprise a tenth of AI startups, but they punch above their weight, attracting a fifth of venture capital investment:

“When it comes to ‘deep tech’ (for example, semiconductors), the US (along with other key countries like South Korea and the UK) remains dominant. This means that China remains heavily dependent on imports for these kinds of technologies. Indeed, China spends seven-times more money on importing semiconductors than it does selling them for export.”

As Ian Hogarth argued in his AI Nationalism essay, “China will certainly try to close this critical trade deficit, and the $140 billion ‘Big Fund’ demonstrates the commitment the government has to narrow the deficit. We also believe that China’s leading technology companies will ramp up their acquisition of deep tech companies from Europe.”

Flag of China

China is making rapid progress in AI, having more or less caught up with the West

Benaich and Hogarth also include predictions in their report. Amongst their 2018 predictions was a merger/acquisition north of $5 billion that would subsequently to be blocked. While this has yet to materialize, the authors still back their predictions. Benaich pointed out that the Chinese technology ecosystem is growing extremely rapidly:

“Of particular note is the ecosystem’s focus on nurturing the growth of AI-first technology companies. By recent counts, China is home to the largest number of AI startups valued over $1 billion. The pace with which these AI startups acquire scale is arguably second to none in the world.

With regards to fundamental research progress, we can consider a) the number of papers accepted into leading academic research conferences, b) the citation count of these papers, and c) the international ranking of universities for related courses such as computer science and engineering.

Looking at the 1st and 2nd measure, China’s contribution to global AI research output is on an upswing. For the thirrd measure, we can see that US and European universities still account for the overwhelming constituency of the top 20 institutions in global rankings. Having said that, Tsinghua University and Peking University are both in the top 20 for computer science and engineering courses.”

Will Europe, or the UK, be the AI R&D lab of the world?

Benaich said that although China is lagging by some measures, the ecosystem is undoubtedly on an upswing in the right direction with immense resources driving its growth. He also noted there is already a firm decoupling between the consumer internet within China and outside of China: Alibaba, Tencent, and Baidu are orders of magnitude more influential in China than Google, Amazon, or Facebook.

This is why Benaich and Hogarth have dedicated an entire section of their report to China. Another part is dedicated to AI and politics. Since Benaich and Hogarth are both based in London, the UK, Benaich’s take on European and British prospects are of particular interest:

“We are in a period of incredible transformation. The economy is changing. Governance is in flux. And the only way we can tackle our toughest societal challenges is with the help of powerful technologies such as AI — workable, safe, ethical AI. That is where Europe’s unique strengths lie, at the fulcrum between China and America’s AI rivalry.”

its-impossible-to-prepare-brexit-delay-f-5cb0ad0cfe727300bade6fc0-1-apr-16-2019-15-29-11-poster.jpg

Europe’s unique strengths lie at the fulcrum between China and America’s AI rivalry, argues Benaich, who also sees a role for post-Brexit UK

Benaich believes the European technology industry has flourished over the past decade, and a new ecosystem with both sophisticated and sustainable financing is emerging:

“This will have a major impact on Europe and Britain’s AI fortunes for years to come. The context is important. At a time of Brexit and a US-China trade war, everyone wonders what Europe’s — and in particular, the UK’s — role will be in the global economy.

Some count it out. Others argue that it will be a leader in ethical business, leveraging the EU’s tough privacy rules implemented last year. But the reality will probably be different: Britain looks set to be the AI R&D lab of the world.

In the past, the main driver was the excellent universities like Oxbridge, Imperial and UCL. They trained the talent that now works at leading US technology companies. But now there’s much more happening. In the last 18 months, US technology companies have made deep inroads into the UK ecosystem to strengthen their AI products.”

The stakes have never been higher

Benaich pointed towards Lyft acquiring Blue Vision Labs for 3D map creation, Niantic acquiring Matrix Mill for real-world mobile AR, Facebook acquiring Bloomsbury.AI for natural language expertise and DeepMind Healthcare folding into the parent company’s healthcare unit.

What’s more, he went on to add, large financing rounds are increasingly available to the best technology companies building intelligent systems in their products. Graphcore secured a $200 million Series D, Darktrace closed a $50 million Series E, and UiPath raised close to $1 billion in three rounds over 12 months.

Naturally, being part of this ecosystem himself, Benaich highlighted that new venture firms built from the ground up for the AI community exist to scout and support exceptional AI talent in Europe. The goal? Building globally competitive companies driven by intelligent systems. Air Street Capital would be a prime example, and it looks like Benaich is on a mission.

In addition to Air Street Capital, he has also founded the Research and Applied AI Summit, which he dubs “a global community of AI entrepreneurs, researchers, and operators who are focused on the science and applications of AI technology.”

Benaich said that over five years, they had attracted founders and leadership from many US technology companies (such as Francois Chollet from Google Brain and Chris Ré from Stanford among others) to speak in London for the first time. They have also showcased early on founders from Graphcore, SwiftKey, Bloomsbury.AI, Benevolent.AI, and LabGenius, who have achieved significant milestones or exited their companies.

Lastly, Benaich’s non-profit, the RAAIS Foundation, exists to support education and research in AI for the common good. The RAAIS Foundation is the first backer of Open Climate Fix and OpenMined, which works on climate change and privacy-preserving AI, respectively.

The reason they are doing all of this? “The stakes have never been higher.”

Content retrieved from: https://www.zdnet.com/article/ai-applications-chips-deep-tech-and-geopolitics-in-2019-the-stakes-have-never-been-higher/.

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AI applications, chips, deep tech, and geopolitics in 2019: The stakes have never been higher

The state of AI in 2019 report analysis with report author, AI expert, and venture capitalist Nathan Benaich continues. High-profile applications, funding, and the politics of AI

It’s the time of the season for AI reports. As we noted earlier, the last few days saw the publication of not 1, but three top-notch reports on the state of AI. People working in VCs have authored all of them, and keeping a close eye on all things AI: From technological breakthroughs to implications in the economy and society at large.

Having covered key technological breakthroughs already, we extend the discussion on the implications of AI with Nathan Benaich, co-author of the State of AI Report 2019, Air Street Capital and RAAIS founder. Benaich co-authored the report with AI angel investor and UCL IIPP visiting professor Ian Hogarth.

Benaich and Hogarth have also drawn on the expertise of prominent figures such as Google AI Researcher and the lead of Keras Deep Learning framework François Chollet, VC and AI thought leader Kai-Fu Lee, and Facebook AI Researcher Sebastian Riedel.

AI applications: RPA and autonomous vehicles

Much of the Q&A with Benaich focused on the geopolitics of AI. That’s not to say Benaich and Hogarth’s report does not cover topics such as talent, infrastructure, or applications — it does, extensively. But with such a full plate, one has to pick.

As far as talent is concerned, there is a consensus among experts: AI talent is highly sought after (and rewarded) and investment in training is on the rise. Nonetheless, the talent shortage in AI continues to be a major bottleneck to the broad adoption of the technology across the industry.

One approach to mitigate this is AutoML, that is to say using machine learning to automate an increasing part of the process of applying machine learning, in a sort of recursive fashion. In the report, AutoML is shown to de-novo design neural networks that are better than those designed by humans to run on resource-constrained mobile devices, for example.

The macro picture remains hot. Funds invested in AI grew by almost 80 percent in 2018 compared to 2017, exceeding $27 billion per year, with North America leading the way at 55 percent market share. Some of the application areas this capital has been pouring into as emphasized in the report are robotics (mainly in manufacturing and logistics), RPA (Robotic Process Automation), healthcare, demand forecasting, autonomous vehicles, and text analysis.

RPA, which is not related to robotics, is “an overnight enterprise success, 15 years in the making”, as the report states. Benaich noted that industry adoption of RPA appears to be growing at a clip, mostly as a result of the benefits it delivers to enterprises: Reduced operating costs and increased operational nimbleness to compete with new entrants.

RPA companies saw massive funding rounds: UiPath raised $800M across two rounds in 2018 and one round in 2019, while Automation Anywhere raised $550 million across two rounds in 2018. As mentioned in FirstMark’s report, however, there are reasons to be cynical about RPA: “RPA, at this stage at least, is more about automation than intelligence, more about rules-based solutions than AI.” Benaich agrees.

Another high-profile area of application is autonomous vehicles (AV). As Benaich and Hogarth note, self-driving cars are now a game for multi-billion-dollar balance sheets. They list spending by the likes of Waymo, Uber, Cruise, and Ford to make their case. But despite growth in investment and live AV pilots in California and elsewhere, some players have missed launch dates, while others remain silent.

Benaich and Hogarth point out that while the average Californian drives 14,435 miles per year, only 11/63 companies had driven more than this in 2018. Waymo drove more than one million miles in 2018, nearly three times as much as second best GM Cruise and 16-times as much as third best Apple. As for Tesla — it does not report its disengagement metrics to the California DMV.

Allegedly, however, Tesla has more data than any of the other players, giving it a leg up in the race. Tesla also designs its own AI chip to power the compute needed on board. This is another red hot area for innovation, as it is driving the capabilities of AI. We have covered some of the pioneers in this space, such as Graphcore, Habana, and GreenWaves.

AI chips, deep tech, geopolitics: China’s rapid growth

Benaich believes the timing is right to develop novel chips that are purpose-built for training and inference of AI models:

“We think this is true because of industry adoption of AI models for several large-scale use cases, especially in consumer internet. As a result, chip designers have a clear customer to design for. Designing chips, however, is an endeavor that is very capital intensive and requires significant domain experience that can only be acquired over many many years.”

This is also closely linked to geopolitics, as per Benaich’s reasoning. Companies building this kind of “deep” or “core” sector-agnostic technology comprise a tenth of AI startups, but they punch above their weight, attracting a fifth of venture capital investment:

“When it comes to ‘deep tech’ (for example, semiconductors), the US (along with other key countries like South Korea and the UK) remains dominant. This means that China remains heavily dependent on imports for these kinds of technologies. Indeed, China spends seven-times more money on importing semiconductors than it does selling them for export.”

As Ian Hogarth argued in his AI Nationalism essay, “China will certainly try to close this critical trade deficit, and the $140 billion ‘Big Fund’ demonstrates the commitment the government has to narrow the deficit. We also believe that China’s leading technology companies will ramp up their acquisition of deep tech companies from Europe.”

Flag of China

China is making rapid progress in AI, having more or less caught up with the West

Benaich and Hogarth also include predictions in their report. Amongst their 2018 predictions was a merger/acquisition north of $5 billion that would subsequently to be blocked. While this has yet to materialize, the authors still back their predictions. Benaich pointed out that the Chinese technology ecosystem is growing extremely rapidly:

“Of particular note is the ecosystem’s focus on nurturing the growth of AI-first technology companies. By recent counts, China is home to the largest number of AI startups valued over $1 billion. The pace with which these AI startups acquire scale is arguably second to none in the world.

With regards to fundamental research progress, we can consider a) the number of papers accepted into leading academic research conferences, b) the citation count of these papers, and c) the international ranking of universities for related courses such as computer science and engineering.

Looking at the 1st and 2nd measure, China’s contribution to global AI research output is on an upswing. For the thirrd measure, we can see that US and European universities still account for the overwhelming constituency of the top 20 institutions in global rankings. Having said that, Tsinghua University and Peking University are both in the top 20 for computer science and engineering courses.”

Will Europe, or the UK, be the AI R&D lab of the world?

Benaich said that although China is lagging by some measures, the ecosystem is undoubtedly on an upswing in the right direction with immense resources driving its growth. He also noted there is already a firm decoupling between the consumer internet within China and outside of China: Alibaba, Tencent, and Baidu are orders of magnitude more influential in China than Google, Amazon, or Facebook.

This is why Benaich and Hogarth have dedicated an entire section of their report to China. Another part is dedicated to AI and politics. Since Benaich and Hogarth are both based in London, the UK, Benaich’s take on European and British prospects are of particular interest:

“We are in a period of incredible transformation. The economy is changing. Governance is in flux. And the only way we can tackle our toughest societal challenges is with the help of powerful technologies such as AI — workable, safe, ethical AI. That is where Europe’s unique strengths lie, at the fulcrum between China and America’s AI rivalry.”

its-impossible-to-prepare-brexit-delay-f-5cb0ad0cfe727300bade6fc0-1-apr-16-2019-15-29-11-poster.jpg

Europe’s unique strengths lie at the fulcrum between China and America’s AI rivalry, argues Benaich, who also sees a role for post-Brexit UK

Benaich believes the European technology industry has flourished over the past decade, and a new ecosystem with both sophisticated and sustainable financing is emerging:

“This will have a major impact on Europe and Britain’s AI fortunes for years to come. The context is important. At a time of Brexit and a US-China trade war, everyone wonders what Europe’s — and in particular, the UK’s — role will be in the global economy.

Some count it out. Others argue that it will be a leader in ethical business, leveraging the EU’s tough privacy rules implemented last year. But the reality will probably be different: Britain looks set to be the AI R&D lab of the world.

In the past, the main driver was the excellent universities like Oxbridge, Imperial and UCL. They trained the talent that now works at leading US technology companies. But now there’s much more happening. In the last 18 months, US technology companies have made deep inroads into the UK ecosystem to strengthen their AI products.”

The stakes have never been higher

Benaich pointed towards Lyft acquiring Blue Vision Labs for 3D map creation, Niantic acquiring Matrix Mill for real-world mobile AR, Facebook acquiring Bloomsbury.AI for natural language expertise and DeepMind Healthcare folding into the parent company’s healthcare unit.

What’s more, he went on to add, large financing rounds are increasingly available to the best technology companies building intelligent systems in their products. Graphcore secured a $200 million Series D, Darktrace closed a $50 million Series E, and UiPath raised close to $1 billion in three rounds over 12 months.

Naturally, being part of this ecosystem himself, Benaich highlighted that new venture firms built from the ground up for the AI community exist to scout and support exceptional AI talent in Europe. The goal? Building globally competitive companies driven by intelligent systems. Air Street Capital would be a prime example, and it looks like Benaich is on a mission.

In addition to Air Street Capital, he has also founded the Research and Applied AI Summit, which he dubs “a global community of AI entrepreneurs, researchers, and operators who are focused on the science and applications of AI technology.”

Benaich said that over five years, they had attracted founders and leadership from many US technology companies (such as Francois Chollet from Google Brain and Chris Ré from Stanford among others) to speak in London for the first time. They have also showcased early on founders from Graphcore, SwiftKey, Bloomsbury.AI, Benevolent.AI, and LabGenius, who have achieved significant milestones or exited their companies.

Lastly, Benaich’s non-profit, the RAAIS Foundation, exists to support education and research in AI for the common good. The RAAIS Foundation is the first backer of Open Climate Fix and OpenMined, which works on climate change and privacy-preserving AI, respectively.

The reason they are doing all of this? “The stakes have never been higher.”

Content retrieved from: https://www.zdnet.com/article/ai-applications-chips-deep-tech-and-geopolitics-in-2019-the-stakes-have-never-been-higher/.

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Open source AI chips making Green Waves: Bringing energy efficiency to IoT architecture

What if machine learning applications on the edge were possible, pushing the limits of size and energy efficiency? GreenWaves is doing this, based on an open-source parallel ultra low power microprocessor architecture. Though it’s early days, implications for IoT architecture and energy efficiency could be dramatic.

The benefits open source offers in terms of innovation and adoption have earned it a place in enterprise software. We could even go as far as to say open source is becoming the norm in enterprise software. But open source hardware, chips to be specific, and AI chips to be even more specific? Is that a thing?

Also: AI Startup Gyrfalcon spins plethora of chips for machine learning 

Apparently it is. GreenWaves, a startup based in Grenoble, France, is doing just that. GreenWaves is developing custom, ultra-low power specialized chips for machine learning. These specialized chips leverage parallelism and a multi-core architecture to run machine learning workloads at the edge, on battery-powered devices with extreme limitations. The chips GreenWaves makes are based on open source designs, and are making waves indeed.

GreenWaves just announced a 7M€ Series A Funding with Huami, Soitec, and other investors. As per the announcement, funds will finance the sales ramp of GreenWaves’ first product, GAP8, and the development of the company’s next generation product. ZDNet discussed with Martin Croome, GreenWaves VP of Product Development, to find out what this is all about.

Open source microprocessors for IoT

First off, what does open source even mean when we’re talking about microprocessors? As Croome explained, what is open source in this case is the instruction set architecture (ISA), and the parallel ultra low power computing platform (PULP) that sits on top of it.

Also: AI Startup Cornami reveals details of neural net chip

GreenWaves is a fabless chip maker. What this means is that it designs chip architectures, and it then builds them by outsourcing to some hardware manufacturer. So, GreenWaves uses low-level building blocks, customizing them and combining them with its own extensions, to produce a proprietary design.

This approach is somewhat reminiscent of open core software: An open source core, with custom extensions that add value. The building blocks that GreenWaves is using is the RISC-V instruction set, and PULP.

PULP is an open source parallel ultra-low power platform on which innovative chip designs can be created.

RISC-V is an open-source hardware ISA based on established reduced instruction set computer (RISC) principles. RISC-V can be used royalty-free for any purpose and began in 2010 at the University of California, Berkeley. RISC-V has many contributors and users in the industry. As Loic Lietar, GreenWaves’ co-founder and CEO noted, the likes of Nvidia and Google also use RISC-V. This means RISC-V contributions grow, and anyone can benefit.

Also: AI chips for big data and machine learning: GPUs, FPGAs

PULP is a parallel ultra-low power multi-core platform aiming to satisfy the computational demands of IoT applications requiring flexible processing of data streams generated by multiple sensors. PULP wants to meet the computational requirements of IoT applications, without exceeding the power envelope of a few milliwatts typical of miniaturized, battery-powered systems.

PULP started as a joint effort between ETH Zürich and the University of Bologna in 2013. GreenWaves sprung out of PULP in 2014, as its CTO and co-founder, Eric Flamand, was also a co-founder in PULP. Fast forward to today, and GreenWaves has 20 employees, shipped a first batch of GAP8 chips to clients, and raised a total of €11.5.

Real-time processing at the edge

Croome noted that GreenWaves needed much less capital than what chip startups usually need, which is mostly spent in getting IP rights for designs. GreenWaves did not have to do this, and this made financing easier. Or, as Lietar put it, a few years ago, when GreenWaves would mention open source chips, there was a good chance they would be thrown out of the room. Not anymore.

Also: AI startup Flex Logix touts vastly higher performance than Nvidia

So, what’s special about GAP8, what can it be used for, and how?

GAP8 has an integrated, hierarchical architecture. It hosts 8 extended RISC-V cores and a HWCE (Hardware Convolution Engine). GreenWaves promises ultra low power 20x better than the state-of-the-art on art on content understanding. Content, in this context, can mean anything from image to sound or vibration sensor input.

What GAP8 is designed to do is to process that data at the edge in real time, bypassing the need to collect and send for processing to some remote data center. In order to do this, it has to be fully programmable, agile, and have low installation and operation cost.

gap8.jpg

GAP8’s architecture

The agile part is there, as GAP8 can wake up from a sleep state in 0.5 milliseconds. As Croome noted, such chips deployed at the edge spend a big part of their lifetime actually doing nothing. So it was important to design something that sleep consuming as little power as possible, and then wake up, and switch modes of operation, as quickly and efficiently as possible.

Also: AI’s insatiable appetite for silicon requires new chips

The low installation and operation cost is there, too, as GreenWaves promises years of operation on batteries, or even better solar cells. As GAP8 can operate over wireless solutions such as LoRa, GreenWaves also promises a 10- to 100- fold cost reduction over wired installations.

So, what can GAP8 do? Clients are using GAP8 for things such as counting people or objects, vibration and sound analysis, object recognition, and more. Some areas of application are smart cities, industry, security, and consumer applications. The really interesting part, however, is in how it all works.

Deploying machine learning models

All these applications are based on using machine learning, and more specifically, neural networks. GAP8 takes care of the inference, which means the models have to be trained first, and then deployed on GAP8. And this is where it gets a bit tricky.

Also: Chip startup Efinix hopes to bootstrap AI efforts in IoT

GAP8 is programmable via C or C++. So how does one get from a model built using TensorFlow, or PyTorch, and other machine learning libraries, to deployment on a GAP8? The software stack for this is open source and available on GitHub.

Examples exist for the development flow from TensorFlow to C. However, there’s a couple of gotchas. First, currently GAP8 only works with TensorFlow. Croome said this a matter of resources and priorities, and integration with other frameworks will be provided as well. For the time being, he added, what people do is to port models created in other frameworks to TensorFlow via ONNX.

Then, if you’re expecting a one-click deployment, you’re in for a disappointment. As Croome explained, the flow is tools based rather than being monolithic. This means that a number of tools provided by GreenWaves have to be utilized in order to deploy models to GAP8.

Croome noted that “all the functionality of GAP8 is visible to the programmer but that we do provide pre-written and optimized code as a ‘starting block’ for getting something up and running quickly. The HWCE accelerates the convolution operation however like all hardware blocks it works on specific convolution types. If it doesn’t match a specific layer then this can always be accelerated on the cluster cores programatically.”

Bringing energy efficiency to IoT architecture

The important thing here, however, is the ability to effectively process data at the edge. With a processor like GAP8, Croome noted, one can analyze the content produced by a rich data sensor and only upload the outcome, for example how many people are in a room:

Also: Meet Jetson Xavier: Nvidia’s AI chip

“This may well be uploaded into a time series database via an IoT Application platform (which may also only be hit after transmission over a low-speed LPWAN type network further minimizing data transfer). The energy spent in doing this analysis and the wireless transmission of the results, which can be seen as an ultimate compression, is far less than the wireless transmission of the raw data.”

greenwaveapps.jpg

Some of the applications low-power AI chips like GAP8 can be used for, simplifying IoT architecture

Although we have seen things such as deploying Hadoop at the edge, this would probably make little sense here. AI algorithms that operate on aggregate data from multiple sensors or access very large databases on the pre-compressed data are clearly better run on generic platforms on the edge (as opposed to very edge) or in the cloud, according to Croome.

“For a one in many face recognition application, the extraction of the key features would be run on GAP8 in the sensing device, the result would be uploaded and the matching would run in the cloud. This would be the best balance from a system point of view, for power consumption and from a SW engineering perspective,” Croome said.

Lietar said GreenWaves has been one step ahead of the market in identifying and serving this segment that is now widely recognized. Croome noted the state of the art in machine learning is evolving rapidly. He went on to add, however, that because GAP8 is not specialized, it can adapt well to new topologies and operators while retaining a best in class energy efficiency.

Innovation that leads to optimized energy efficiency and can simplify technical architecture – what’s not to like?

Content retrieved from: https://www.zdnet.com/article/open-source-ai-chips-making-green-waves-bringing-energy-efficiency-to-iot-architecture/.

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From big data to AI: Where are we now, and what is the road forward?

It took AI just a couple of years to go from undercurrent to mainstream. But despite rapid progress on many fronts, AI still is something few understand and fewer yet can master. Here are some pointers on how to make it work for you, regardless of where you are in your AI journey.

In 2016, the AI hype was just beginning, and many people were still cautious when mentioning the term “AI”. After all, many of us have been indoctrinated for years to avoid this term, as something that had spread confusion, over-promised, and under-delivered. As it turned out, the path from big data and analytics to AI is a natural one.

Not just because it helps people relate and adjust their mental models, or because big data and analytics were enjoying the kind of hype AI has now, before they were overshadowed by AI. But mostly because it takes data — big or not-so-big — to build AI.

It also takes some other key ingredients. So, let’s revisit Big Data Spain (BDS), one of the biggest and most forward-thinking events in Europe, which marked the transition from big data to AI a couple of years back, and try to answer some questions on AI based on what we got from its stellar lineup and lively crowd last week.

Can you fake it till you make it?

Short answer: No, not really. One of the points in that Gartner analytics maturity model was that if you want to build AI capabilities (the predictive and prescriptive end of the spectrum), you have to do it on a solid big data foundation (the descriptive and diagnostic end of the spectrum).

Part of that is all about the ability to store and process massive amounts of data, but that really is just the tip of the iceberg. Technical solutions for this are in abundance these days, but as fellow ZDNet contributor Tony Baer put it, to build AI, you should not forget about people and processes.

More concretely: Don’t forget about data literacy and data governance in your organization. It has been pointed out time and again, but these really are table stakes. So, if you think you can develop AI solutions in your organization by somehow leapfrogging the evolutionary chain of analytics, better think again.

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Gartner’s analytics maturity model may be a good starting point to explain and prepare the transition to AI. (Image: Gartner)

As Oscar Mendez, Stratio CEO, emphasized in his keynote, to go beyond flashy AI with often poor underpinnings, a holistic approach is needed. Getting your data infrastructure and governance right, and finding and training the right machine learning (ML) models on this can yield impressive results. But there is a limit to how far these can take you, amply demonstrated by everyday fails by the likes of Alexa, Cortana, and Siri.

The key message here is that bringing context and reasoning capabilities in play is needed to more closely emulate human intelligence. Mendez is not alone in this, as this is something shared by AI researchers such as Yoshua Bengio, one of Deep Learning’s top minds. Deep Learning (DL) excels in pattern matching, and the data and compute explosion can make it outperform humans in tasks based on pattern matching.

Intelligence, however, is not all about pattern matching. Reasoning capabilities cannot be built on ML approaches alone — at least not for the time being. So what is needed is a way to integrate less hyped AI approaches, of the so-called symbolic line: Knowledge representation and reasoning, ontologies, and the like. This is a message we have been advocating, and to see it take center-stage in BDS was an affirmation.

Should you outsource it?

Short answer: Perhaps, but you should be very considerate about it. So, let’s not beat around the bush: AI is hard. Yes, you should definitely build on foundational capabilities such as data governance, because this is good for your organization anyway. Some, like Telefonica, have managed to get from Big Data to AI, by executing strategic initiatives. But it’s no easy feat.

This point has been validated by what is probably the most reliable survey for ML adoption, answered by more than 11K respondents. Paco Nathan from Derwen presented results and insights from an O’Reilly survey he has instrumented, which more or less confirmed what we knew: There is a growing gap between the AI haves and have-nots.

On the one end of the spectrum, we have the Googles and Microsofts of the world: Organizations applying AI as a core element of their strategy and operation. Their resources, data, and know-how are such that they are leading the AI race. And then there are also adopters, working on applying AI in their domains, and laggards, buried too deep in technical debt to be able to do anything meaningful in terms of AI adoption.

p0043.jpg Leaders, adopters, laggards — the machine learning version. (Image: Paco Nathan / Derwen)

AI leaders have offerings that, on the face of it, seem to “democratize” AI. Both Google and Microsoft presented those in BDS, showcasing, for example, demos in which an image recognition application was built in a point and click fashion in a few minutes.

The message was clear: Let us worry about models and training, and you focus on the specifics of your domain. We can identify mechanical parts, for example — just feed us with your specific mechanical parts, and you are good to go.

Google also announced some new offerings in BDS: Kubeflow and AI Hub. The idea behind them is to orchestrate ML pipelines similarly to what Kubernetes does for Docker containers for applications, and to become a Github for ML models, respectively. These are not the only offerings that promise similar advantages. They sound alluring, but should you use them?

Who would not want to jump the AI queue, and get results here and now without all the hassle, right? This is indeed a pragmatic approach, and one that can get you ahead of the competition. There’s just one problem there: If you outsource your AI entirely, you are not going to develop the skills required to be self-sufficient in the mid-to-long term.

Think of Digital Transformation. Yes, going digital, exploring technologies and redesigning processes is hard. Not all organizations got it, or dedicated enough resources to it. But the ones that did are now ahead of the curve. AI has similar, if not greater, potential to disrupt and differentiate. So, while getting immediate results is great, investment in AI should still be seen as a strategic priority.

The one part you can be less skeptical about outsourcing is infrastructure. For most organizations, the numbers of maintaining your own infrastructure just don’t add up at this point. The economy of scale, head start, and peace of mind that running your infrastructure in the cloud can give are substantial benefits.

Where do we go from here?

Short answer: To the moon and back. It seems like the ML feedback loop is in full swing. So, while adopters are trying to keep up and laggards keep lagging, leaders are getting more and more advanced.

As pointed out in Google Partner Engineering Iberia / Italy Pablo Carrier’s presentation, compute is going to grow exponentially if you try to improve accuracy in DL linearly. In the past six years there was a 10-million fold increase in compute. That’s hard to keep up with even if you are Google Cloud, let alone if you are not.

A rising trend in DL is distribution. In an overview shown in another Google presentation, by Viacheslav Kovalevskyi, technical lead at Google Cloud AI, a word of warning was shared before embarking on the theory and practice of distributed DL: If possible, avoid it. If you really must do it, be aware there is an overhead associated with distribution, and be prepared to pay the price, both in terms of compute and complexity and in terms of footing bills.

Kovalevskyi offered a historical perspective on the different ways of using distributed DL — distributing the data, the model, or both. Distributing data is the easiest approach, distributing both is the hardest. But, in any case, distributed DL is “fairy tale zone” — you will not get a k-times increase in performance by increasing your compute times k.

Of course, Google’s presentation was focused on TensorFlow on Google Cloud, but this is not the only way to go. Databricks has just announced support for HorovodRunner to faciliate distributed DL using Horovod. Horovod is an open source framework, introduced by Uber, also utilized by Google. It’s not the only game in town though.

In a presentation given by Marck Vaisman, Microsoft data scientist and Azure data/AI technical specialist, alternatives were presented, using both Python and R without Spark in the mix. Dask, an open source library for Python, was highlighted. Dask promises advanced parallelism for analytics, working in tandem with projects like Numpy, Pandas, and Scikit-Learn.

And last but not least, graphs and graph databases were also a key theme throughout BDS: Microsoft’s knowledge graph, AWS Neptune, and Oracle Labs using graph analytics with Notebooks. As this is a topic we are following closely, we’ll be revisiting it shortly. Another Google insight to mention here: an internal analysis showed that most of Google’s ML models operate on structured data.

Cloud, distribution, and bringing structure to ML via graphs are some key themes to keep in mind for the future. We will continue to cover those as the journey from Big Data to AI progresses.

Content retrieved from: https://www.zdnet.com/article/from-big-data-to-ai-where-are-we-now-and-what-is-the-road-forward/.