O&G giant Total and Google Cloud are launching a new artificial intelligence (AI) tool to help accelerate the deployment of residential solar panels.

Together they have developed Solar Mapper, a tool that can provide accurate and rapid estimates for the solar potential of homes using a brand-new AI algorithm.

The pair have claimed it can provide better results than similar tools due to the quality of the data extracted from satellite images, the sharpness of the estimation of the solar potential, the relevance of the technology to be installed and the global geographical cover of the tool.

Solar Mapper is set to be rolled out through Europe initially, before going global. It can provide more than 90% geographical cover of France – Total’s home market – encouraging more people to assess the solar potential of their homes.

“Solar Mapper will enable Total to faster deploy solar panels on the houses’ roofs, in order to provide its customers with more affordable and more accessible solar energy,” said Marie-Noëlle Séméria, Total’s chief technology officer.

“By combining Total’s expertise in solar energy with Google Cloud’s expertise in artificial intelligence and databases, we were able to develop an attractive and innovative offer together in just 6 months.”

The tool will initially just be for residential buildings, but Total has said it is aiming to develop a version for industrial and commercial buildings in the future.

Solar Mapper is a further move by the French O&G company to establish itself within the renewable energy sector, having announced its net zero emission by 2050 ambition in May.

This is not the first tool launched by Google to evaluate the solar potential of properties, having brought its Sunroof platform to British households together with E.On in 2018.  

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You may not realize this, but you interact with AI technology on a consistent, if not daily basis. And if you do recognize it, chances are good that you take it for granted. Whether it’s a Spotify playlist, an Alexa reply, or one of the myriad cool things Google Assistant does, it’s powered by AI and cloud technology.

More and more, companies are turning to cloud technology for AI applications, and that means the demand for developers and architects is steadily rising.

The Google Cloud Platform, one of the largest in the space, is a suite of computing services and tools that power Google’s Search, YouTube, and much more. According to Glassdoor, a GCP Cloud Architect can pull in a starting salary of $120,000- $160,000. Ready for a piece of that?

Google Cloud computing isn’t exactly something you master overnight. Hell, it could take you weeks just to form a basic understanding of it. It takes time to learn topics like deploying and implementing cloud solutions, software-defined networking, or virtual private clouds.

Fortunately, you can kick-start your education with some online training. Take the Google Cloud Certifications Practice Tests + Courses Bundle, for instance. This comprehensive online training features 43 hours of lectures and other tools to help prepare you for a career in the emerging field.

Sign up, and you’ll get lifetime access to the training so feel free to really dig in and learn things. Or, if you’re like many of us, drop in and out and spend the rest of the pandemic period fine-tuning yourself.

Considering how incredibly valuable the information in this 7-course bundle is, $29.99 is a small price to pay. It’s worth more than $630 if you were to purchase yourself, but we’d never let you pay that much.

Save even more!

In addition to the savings above, when you buy through AndroidGuys Deals, for every $25 spent, you get $1 credit added to your account. What’s more, should you refer the deal via social media or an email that results in a purchase, you’ll earn $10 credit in your account.

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Machine learning, a sub-component of artificial intelligence, is not new to the enterprise. But with techniques like deep learning, emulating human brain actions, increasingly gaining traction, businesses are identifying new and potentially transformative deployments of digitally disruptive technologies.

According to Algorithmia’s 2020 report, the main use cases for machine learning translate to customer service (i.e. chatbots) and internal cost reduction. But machine learning has applications far and wide. Dynamic pricing or surge pricing is essentially ML models that learn from corresponding factors that include customer interest, demand and history to adjust prices and entice purchases. Churn modelling is another application in telecom analytics where Machine Learning is deployed to predict which customers are likely to be lost and allowing corrective measures to be undertaken to mitigate the churn.

Currently, to Ensure Business Continuity in the Covid-19 era, more and more businesses are moving to the cloud, and the cloud is making AI and Machine Learning more accessible to the enterprise. Here are a few cloud deployments that find enterprise adaptability-

• AWS

Amazon’s cloud service, AWS offers a wide range of machine learning solutions on the cloud, with Amazon claiming that more machine learning happens on its platform than anywhere else. Of particular note is Amazon SageMaker, which is focused on simplifying the process of building, training and deploying machine learning models. It does this in part through a web-based visual interface allowing for the uploading of data, the tuning of models and comparisons of performance.

AWS has also developed specific hardware for machine learning, with an inference chip known as Inferentia, which is intended for sophisticated applications such as search recommendations, dynamic pricing and automated customer support, and is accessible through the cloud.

• Google Cloud

Google is perhaps the company most associated with machine learning, thanks to its development of the open-source TensorFlow platform, as well as its association with one of the most advanced machine learning companies – DeepMind and its programs such as AlphaGo.

Intended for enterprise use, Google Cloud’s AI Platform combines and integrates different aspects of the machine learning pipeline, from data storage and labelling to training to deployment.

• Microsoft Azure

Microsoft’s Azure cloud platform has built-in machine learning services for enterprises looking to bring machine learning models to bear. With a stated focus on MLOps, the subset of DevOps dealing with correct machine learning development practices, it includes both code-based and drag-and-drop environments to accommodate users of all skill levels.

Azure also has a focus on the potential perils of machine learning, building in so-called ‘responsible machine learning’ solutions to mitigate bias in models.

Summing up, with the proliferation of machine learning services on the cloud critically becoming indispensable to push down operational costs and opening up possibilities, expect enterprises to leverage the technology going forwards. ML will open up new methods of customer interaction, as chatbots are proving, and highlighting areas in need of efficiency, are enterprises ready for this massive change?

The post BRINGING AI AND MACHINE LEARNING ACCESSIBLE TO ENTERPRISES CREDIT TO CLOUD appeared first on Artificial Intelligence.

For those unaware, Artificial Intelligence alludes the re-enactment of human insight into machine so as to enable them to think like members of the human race. Thus, attributes like problem solving, learning and critical thinking are carried on by machines.

Artificial intelligence brings along a colossal potential to the table which is ultimately sculpturing the fate of technology in future.

Thus, its no surprise that business industry is investing more and more in this platform that holds the promise of changing the world as we know it. Thus as per estimates, given the investments that artificial intelligence is witnessing, it will easily cross the value of trillions of dollar in the future.

AI also houses gigantic potential when it comes to development of software and its further improvement. It is not a replacement for human intelligence but it eliminates the error that humans might make and does the task with complete accuracy and precision.

With the mankind being largely dependent on artificial intelligence, here is a list of AI platforms that are pulling the strings in the industry

Google Cloud AI Platform:

This platforms allows developers to effortlessly manufacture artificial intelligence models. It incorporates Google Cloud Dataflow. It allows pre-handling and permits the users to get data from Google Cloud Storage, Google BigQuery etc.

Tasks like NLP, Speech, Vision Capabilities and deep learning are carried out through the help of Google Cloud AI platform. These incorporate-

Machine Learning

By providing a toolchain, the platform lets developers without much difficulty create AI models and assist in the procedure of development. 

Deep Learning

Deep learning applications are developed with ease due to pre-configured Virtual Machines (VMs) offered by this platform. Deep Learning containers also provide flexibility by being compatible with TenserFlow, PyTorch.

Natural Language Processing (NLP)

Google provides Natural Language Processing capacities to the developers so as to discover the composition and significance of a text. It is of utmost importance to break down content with Google NLP API that is accessible via RESTful API.

Vision

Vision is also a part of Google Cloud AI platform. It gives users the oppotutnity to detect images and get astute bits of knowledge from them. For this purpose, RPC and REST APIs are used and their integration with ML models helps identifying items, faces and texts.

Speech

In order to convert speech to text and text to speech, Google Speech API is used. It utilizes neural network models. Speech to text API bolsters 120 languages.

All thanks to voice recognition, it lets developers and engineers empower their applications and software with voice command features. Speech is very beneficial when it comes to tasks like transforming texts to audio for example in mp3 format.

Microsoft Azure AI Platform:

By providing developers with AI competence to do speech recognition, Machine learning (ML), object recognition, machine translation and Knowledge mining; Microsoft Azure AI Platform has established itself as one of the most popular platforms worldwide. Tools like Bot Framework, Cognitive Services, Azure Machine Learning to create new exposure for the users.

It also incorporates Python based ML service known as Azure Databricks which amalgamates ONNX and Azure ML.

IBM Watson:

IBM Watson is an open AI that has made performing in several fields like financial services, Internet of Things (IoT), media, healthcare, oil & gas sector and advertising possible and an advanced mean.

It lets engineers speeding up the turn of events and enlargement of AI application models.

Apparatuses like SDKs are provided by IBM Watson. Watson Assistant can also be used to emulate a conversation between a user and the machine.

IBM watson is capable of converting speech to text through Natural Language Processing (NLP) which is also known as Watson Natural Language Understanding (NLU). Thus, software makers are greatly benefitted by this.

IBM Watson also offers its developers SDKs for Java, Python, Swift, Ruby,

and Node.js which empowers the engineers and developers to locate an appropriate SDK for their tasks.

It is used widely in the medical field as it lets doctors read Xray and MRI scans precisely.

BigML:

As the name suggests, BigML provides users with ML calculations. Most notable programming languages like Java, Python, Ruby, Swift, Node.js etc as per the requirement of the task and the developers.

Features like loading of data, an expansive room of no cost models to utilise and flexible, elastic pricing make BigML a widely used Artificial intelligence platform throughout the world.

Infosys Nia:

With its advanced technology, Infosys Nia permits clients to manufacture custom encounters to match the industrial requirement and models. It includes features like–

Machine Learning

BigML offers a massively expansive scope of Machine Learning calculations which speeds the very process and makes it uncomplicated for the developers to create software.

Nia Chatbot

Assembling Artificial intelligence based chatbots helps the developers enable technologies to converse with human beings and complete actions that they are told to do. 

Contracts Analysis

By incorporating Deep Learning, ML, and semantic modelling provides developers with a wide scope for contracts analysis 

Data nia

Data Nia is a data driven tool which helps a firm receive insights and therefore forecast the plans.

The post Best of Artificial Intelligence Platforms in the world appeared first on Artificial Intelligence.

In an effort to help lenders expedite the processing of applications for the U.S. Small Business Administration’s (SBA) Paycheck Protection Program, which aims to keep workers employed during the coronavirus pandemic, Google developed an AI solution called PPP Lending AI that integrates with existing document ingestion tools. It’s available to eligible lending institutions through June 30.

As Google explains in a whitepaper, AI can automate the handling of volumes of loan applications by identifying patterns that would take a human worker longer to spot. Specifically, PPP Lending AI can classify and extract data in critical paperwork before readying documents for submission to the SBA.

PPP Lending AI, which Google says takes only days to implement, is a solution in three parts.

The first is the Loan Processing Portal, a web-based app that serves as a user interface and self-servicing center. In addition to providing administration views for loan officers and loan processors, it allows end users and loan applicants to create, submit, and view the status of their PPP loan.

The second piece of PPP Lending AI is the Document AI PPP Parser, which allows lenders to use AI to extract structured information from loan documents submitted by the loan applicants. It’s built atop Google Cloud‘s Document AI, a service that leverages optical character recognition, form parsing, and natural language processing to capture and enrich unstructured data.

The third is Loan Analytics, which lets servicers or lenders onboard structured historical loan data, perform de-identification anonymization on sensitive information, store it securely with fine-grained data access control, and perform queries on the data.

“Leveraging artificial intelligence, we’ve created an end-to-end solution that speeds up the time-to-decision on loans and helps inform lenders’ liquidity analysis — from the initial application submission to the underwriting process and SBA validation,” wrote Google Cloud global financial services and solutions lead Christin Brown in a blog post. “The solution is also equipped with Google’s security capabilities, enabling lenders to meet policy requirements and protect critical assets.”

Google says lenders can speak with a Google Cloud account manager for more information.

PPP Lending AI appears to skirt around a newly imposed U.S. Treasury and SBA rule prohibiting the submission of PPA loans prepared by robotic process automation (RPA), or AI systems that perform repetitive, monotonous tasks at scale with greater speed and accuracy than humans. The agencies blamed RPA for overburdening E-Tran, the SBA’s electronic loan servicing portal, and reducing its capabilities.

On Monday, E-Tran crashed minutes after the opening of $310 billion in additional PPP funding. The funds were approved last week following the first $349 billion round, which ran out in early April. That’s in spite of the fact that the SBA limited application submissions to 350 per hour and allowed banks with a minimum of 5,000 loans to bulk-file their applications.

PPP loans are available to small businesses that were in operation as of February 15 with 500 or fewer employees, including not-for-profits, veterans’ organizations, tribal concerns, self-employed individuals, sole proprietorships, and independent contractors. Businesses with more than 500 employees in certain industries can also apply for loans, according to the SBA and Treasury.

The post Google releases AI tool for processing Paycheck Protection Program loans appeared first on Artificial Intelligence.

A preprint study published this week by coauthors at Google Research describes Entities as Experts (EAE), a new type of machine learning model that can access memories of entities (e.g., people, places, organizations, dates, times, and figures) mentioned in a piece of sample text. They claim it outperforms two state-of-the-art models with far less data while capturing more factual knowledge and is more modular and interpretable than the Transformers architecture on which it’s based.

If peer review bears out the researchers’ claims about EAE, it could solve a longstanding natural language processing challenge: acquiring the knowledge needed to answer questions about the world without injecting entity-specific knowledge. In enterprise environments, EAE could form the foundation for chatbots that ingest corpora of domain-specific information and respond to questions about the corpora with information that’s most likely to be relevant.

EAE contains neurons (mathematical functions) arranged in layers that transmit signals from input data and adjust the strength (weights) of each connection, as with all deep neural networks. That’s how it extracts features and learns to make predictions, but because EAE is based on the Transformer architecture, it has attention. This means every output element is connected to every input element and the weightings between them are calculated dynamically.

Uniquely, EAE also contains entity memory layers that enable it to “understand” and respond to questions about text in a highly data-efficient way. The model learns knowledge directly from text, along with other model parameters (i.e., configuration variables estimated from data and required by the model when making predictions) and associates memories with specific entities, or data types like titles and numeric expressions.

As the coauthors explain “[For example,] a traditional Transformer would need to build an internal representation of Charles Darwin from the words ‘Charles’ and ‘Darwin,’ both of which can also be used in reference to very different entities, such as the Charles River or Darwin City. Conversely, EAE can access a dedicated representation of ‘Charles Darwin,’ which is a memory of all of the contexts in which this entity has previously been mentioned. This representation can also be accessed for other mentions of Darwin, such as ‘Charles Robert Darwin’ or ‘the father of natural selection.’ Having retrieved and re-integrated this memory, it is much easier for EAE to relate the question to the answer.”

To evaluate EAE, the researchers trained the model on Wikipedia articles, scraping hyperlinks to the articles and the Google Cloud Natural Language API for a total of 32 million contexts paired with over 17 million entity mentions. They kept only the top 1 million most frequent entities and reserved 0.4% of them for development and testing purposes. Then they pretrained the model from scratch for 1 million steps and fine-tuned the pretrained EAE over the course of 50,000 training steps on TriviaQA, a reading comprehension task in which questions are paired with documents. (From TriviaQA, 77% of training examples were kept — those that weren’t an entity were discarded.)

The team reports that on several “cloze” tests, where the model had to recover the words in a blanked-out mention by correctly associating the mention with its surrounding sentence context, EAE used only a small proportion of its parameters at inference time — roughly the top 100 entities for each mention in a given question — versus a baseline Transformer model. (The Transformer model used nearly 30 times the number of parameters.) Preliminary evidence also suggests that EAE had more factual knowledge than a baseline BERT model.

“[Our] analysis shows that the correct identification and reintegration of entity representations is essential for EAE’s performance,” wrote the coauthors. “Training EAE to focus on entities is better than a similar-sized network with an unconstrained memory store.”

The post Google’s Entities as Experts AI answers text-based questions with less data appeared first on Artificial Intelligence.

Don’t look now, but object stores – those vast repositories of data sitting behind an S3 API – are beginning to resemble databases. They’re obviously still separate categories today, but as the next-generation data architecture takes shape to solve emerging real-time data processing and machine learning challenges, the lines separating things like object stores, databases, and streaming data frameworks will begin to blur.

Object stores have become the primary repository for the vast amounts of less-structured data that’s generated today. Organizations clearly are using object-based data lakes in the cloud and on premise to store unstructured data, like images and video. But they’re also using them to store many of the other types of data, like sensor and log data from mobile and IoT devices, that the world is generating.

The object store is becoming a general purpose data repository, and along the way it’s getting closer to the most popular data workloads, including SQL-based analytics and machine learning. The folks at object storage software vendor Cloudian are moving their wares in that direction too, according to Cloudian CTO Gary Ogasawara.

“We’re moving more and more to that,” Ogasawara tells Datanami. “If you can combine the best of both worlds – have the huge capacity of an object store and the advanced query capability of an SQL-type database – that would be the ideal. That’s what people are really asking for.”

Past Is Prologue

We’ve seen this film before. When Apache Hadoop was the hot storage repository for big data (really, less-structured data), the first big community efforts was to develop a relational database for it. That way, data analysts with existing SQL skills – as well as BI applications expecting SQL data – would be able to leverage it without extensive retraining. And besides, after running less-structured data through MapReduce jobs, you  needed a place to put the structured data. A database is that logical place.

This led to the creation of Apache Hive out of Facebook, and the community followed with a host of other SQL-on-Hadoop engines (or relational databases, if you like), including Apache Impala, Presto, and Spark SQL, among others. Of course, Hadoop’s momentum fizzled over the past few years, in part due to the rise of S3 from Amazon Web Services and other cloud-based object storage systems, notably Azure BLOB Storage from Microsoft and Google Cloud Storage, which are universally more user-friendly than Hadoop, if not always cheaper.

In the cloud, users are presented with a wide range of specialty storage repositories and processing engines for SQL and machine learning. On the SQL front, you have Amazon RedShift, Azure Data Warehouse, and Google BigQuery. On top of these “native” offerings, the big data community has adapted many existing and popular analytics databases, including Teradata, Vertica, and others, to work with S3 and other object stores with an S3-compatible API.

The same goes for machine learning workloads. Once the data is in S3 (or Blob Store or Google Cloud Storage), it’s a relatively simple manner to use that data to build and train machine learning models in SageMaker, Azure Machine Learning, or Google Cloud AutoML. With the rise of the cloud, every member of the big data and machine learning community has moved to support the cloud, and with it object storage systems.

As the cloud’s momentum grows, S3 has become the defacto data access standard for the next generation of applications, from SQL analytics and machine learning to more traditional apps too. For many new applications, data is simply expected to be stored in an object storage system, and developers expect to be able to access that data over the S3 API.

A Hybrid Architecture

But of course, not all new applications will live on the cloud with ready access to petabytes of data and gigaflops of computing power. In fact, with the rise of 5G networks and the explosion of smart devices on the Internet of Things (IoT), the physical world is the next frontier for computing, and that’s changing the dynamics for data architects who are trying to foresee new trends.

At Cloudian, Ogasawara and his team are working on adapting its HyperStore object storage architecture to fit into the emerging edge-and-hub computing model. One of the examples he uses is the case of an autonomous car. With cameras, LIDAR, and other sensors, each self-driving car generates terabytes worth of data every day, and petabytes per year.

“That is all being generated at the edge,” he says. “Even with a 5G network, you will never be able to transmit all that data to somewhere else for analyses. You have to push that storage and processing as close to the edge as possible.”

Cloudian is currently working on developing a version of HyperStore that sits on the edge. In the self-driving car example, the local version of HyperStore would run right on the car and assist with storing and processing data coming off the sensors in real time. This computing environment would constitute a fast “inner loop,” Ogasawara says.

“But then you have a slower outer loop that’s also collecting data, and that includes the hub where the large, vast data lake resides in object storage,” he continues. “Here you can do more extensively training of ML models, for example, and then push that kind of metadata out to the edge, where it’s essentially a compiled version of your model that can be used very quickly.”

In the old days, object stores resembled relatively simple (and nearly infinitely scalable) key-value stores. But to support future use cases — like self-driving cars as well as weather modeling and genomics — the object store needs to learn new tricks, like how to stream data in and intelligently filter it so that only a subset of the most important data is forwarded from the edge to the hub.

To that end, Cloudian is working on a new project that will incorporate analytics capabilities. It has a working name of the Hyperstore Analytics Platform, the project would incorporate frameworks like Spark or TensorFlow to assist with the intelligent streaming and processing of data. A beta was expected by the end of the year (at least that was the timeline that Ogasawara shared in early March before the COVID-19 lockdown.)

Object’s Evolution

Cloudian is not the only object storage vendor looking at how to evolve its product to adapt to emerging data challenges. In fact, its not just object storage vendors who are trying to tackle the probolem.

The folks at Confluent have adapted their Kafka-based stream processing technologies (which excel at processing event data) to work more like a database, which is good at managing stateful data. MinIO has SQL extensions that allow its object store to function like a database. NewSQL database vendor MemSQL has long had hooks for Kafka that allow it to process large amounts of real-time data. The in-memory data grid (IMDG) vendors are doing similar things for processing new event data within the context of historic, stateful data. And let’s not even get into how the event meshes are solving this problem.

According to Ogasawara, adapting Cloudian’s HyperStore offering is a logical way to tackle today’s emerging data challenges. “You’ve done very well at building this storage infrastructure,” he says. “Now, how do you make the data usable and consumable? It’s really about providing better access APIs to get to that data, and almost making the object storage more intelligent.”

Object stores are moving beyond their initial use case, which was reading, writing, and deleting data at massive scale. Now customers are pushing object storage vendors to support more advanced workflows, including complex machine learning workflows. That will most likely require an extension to the S3 API (something that Cloudian has brought up with AWS, but without much success).

“How do you look into those objects? Those types of APIs need more and more [capabilities],” Ogasawara says. “And even letting AI or machine learning-type workflows, doing things like a sequence of operations — those types of language constructs, everyone is starting to look at and trying to figure out how do we make it easier for users and customers to make that data analysis possible.”

The post Object Stores Starting to Look Like Databases appeared first on Artificial Intelligence.

Digital transformation and analytics are nearly inseparable. “At the core of any successful digital transformation is the ability to leverage the company’s data assets to drive superior customer experiences, products and services as well as operating model efficiencies,” says Scott Snyder, a Digital and Innovation Partner with Heidrick & Struggles, and co-author of “Goliath’s Revenge: How Established Companies Turn the Tables on Digital Disruptors.”

Companies typically need data science know-how in order to connect data to analytics or algorithms and deliver digital insight. “Without a critical mass of these data science and analytics skills, companies will struggle to keep up with both customer expectations and new innovation opportunities,” Snyder says.

The gap between supply of and demand for data sciences skills is a problem IT leaders know well. One the one hand, data is growing at an exponential rate. “It’s widely reported that 90 percent of the world’s data has been generated in the last two years, and with data doubling every 1.2 years on average versus processing speed only doubling every one to 1.5 years, companies must become more efficient at analyzing data to keep up,” says Snyder.

The post Digital transformation: 6 ways to democratize data skills appeared first on Artificial Intelligence.

Treehouse Software, Inc. is pleased to announce an agreement with Google Cloud as a technology partner in the Google Cloud Partner Advantage Program. As a technology partner, Treehouse Software Inc. will now offer enterprise customers a comprehensive Mainframe-to-Google Cloud data replication and migration solution. This relationship provides Google Cloud customers with Treehouse’s combination of decades worth of mainframe systems experience and comprehensive data replication capabilities, with Google Cloud’s platform.

The Google Cloud Enterprise Transformation Practice assists companies in migrating and modernizing workloads on Google Cloud’s global, secure, and reliable platform. Once an enterprise’s data is on Google Cloud, they can immediately take advantage of some of the most advanced artificial intelligence, machine learning, big data analytics, and data warehousing services in the world.

The same technology that supports Google’s global network protects data while meeting rigorous industry-specific compliance standards, and Treehouse Software’s tcVISION product moves or syncs mainframe data with real-time and bi-directional data replication. tcVISION’s GUI modeling and mapping, and ease of migrating data to Google Cloud, makes it an ideal choice for modernizing large mainframe environments.

“Through this exciting new collaboration with Google Cloud, Treehouse Software expands its mature and proven mainframe data delivery capabilities, and customers benefit from modernization of their data on one of the most popular and advanced Cloud platforms in the world,” said Joseph Brady, Director of Business Development and Cloud Alliance Lead at Treehouse Software.

The post Treehouse Software Collaborates with Google Cloud to Offer Mainframe-To-Google Cloud Data Replication appeared first on Artificial Intelligence.

Members of the Google Brain team and Google AI this week open-sourced EfficientDet, an AI tool that achieves state-of-the-art object detection while using less compute. Creators of the system say it also achieves faster performance when used with CPUs or GPUs than other popular objection detection models like YOLO or AmoebaNet.

When tasked with semantic segmentation, another task related to object detection, EfficientDet also achieves exceptional performance. Semantic segmentation experiments were conducted with the PASCAL visual object challenge data set.

EfficientDet is the next-generation version of EfficientNet, a family of advanced object detection models made available last year for Coral boards. Google engineers Mingxing Tan, Google Ruoming Pang, and Quoc Le detailed EfficientDet in a paper first published last fall, but revised and updated it on Sunday to include code.

“Aiming at optimizing both accuracy and efficiency, we would like to develop a family of models that can meet a wide spectrum of resource constraints,” the paper, which examines neural network architecture design for object detection, reads.

Authors say existing methods of scaling object detection often sacrifice accuracy or can be resource intensive. EfficientDet achieves its less expensive and resource-hungry way to deploy object detection on the edge or in the cloud with a method that “uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.”

“The large model sizes and expensive computation costs deter their deployment in many real-world applications such as robotics and self-driving cars where model size and latency are highly constrained,” the paper reads. “Given these real-world resource constraints, model efficiency becomes increasingly important for object detection.”

Optimizations for EfficientDet takes inspiration from Tan and Le’s original work on EfficientNet. and proposes joint compound scaling for backbone and feature networks. In EfficientDet, a bidirectional feature pyramid network (BiFPN) acts as a feature network, and an ImageNet pretrained EfficientNet acts as the backbone network.

EfficientDet optimizes for cross-scale connections in part by removing nodes that only have one input edge to create a simpler bidirectional network. It also relies on the one-stage detector paradigm, an object detector known for efficiency and simplicity.

“We propose to add an additional weight for each input during feature fusion, and let the network to learn the importance of each input feature,” the paper reads.

This is the latest object detection news from Google, whose Google Cloud Vision system for object detection recently removed male and female label options for its publicly available API.

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