Here are the top 5 PHP frameworks that every developer should learn:

1. Laravel

Laravel is a powerful, full-stack PHP framework with a great community and a focus on elegance and simplicity. It is a popular choice for web development projects of all sizes, from small blogs to large enterprise applications. Laravel is known for its expressive syntax, powerful features, and large ecosystem of libraries and packages.

2. Symfony

Symfony is another popular PHP framework with a strong focus on modularity and flexibility. It is the foundation for many other PHP frameworks, including Drupal and Magento. Symfony is a good choice for developers who need a framework that is highly customizable and can be adapted to a wide range of projects.

3. Yii

Yii is a high-performance PHP framework that is known for its speed and scalability. It is a good choice for developers who need to build web applications that can handle a lot of traffic. Yii is also a good choice for developers who are looking for a framework with a large and active community.

4. CodeIgniter

CodeIgniter is a lightweight PHP framework that is easy to learn and use. It is a good choice for developers who are just starting out with PHP or who need to build a simple web application quickly. CodeIgniter is also a good choice for developers who are looking for a framework with a small footprint.

5. Slim

Slim is a micro-framework that is designed to be as small and simple as possible. It is a good choice for developers who need to build small, RESTful APIs or web applications. Slim is also a good choice for developers who are looking for a framework that is easy to learn and use.

These are just a few of the many great PHP frameworks that are available. The best framework for you will depend on your specific needs and preferences.

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AI developer AutoBrains claims to have developed an unsupervised-AI system for the automotive industry, which promises a faster technology trajectory for ADAS and AV system development. Tier 1 supplier Continental is partnering with the company to bring the technology to market.

According to AutoBrains, its unsupervised AI operates in a fundamentally different way from traditional deep learning systems currently used in most ADAS. The company says its process is based on multi-disciplinary research and development that allows the system to independently learn, process and interpret relevant data from the car’s surroundings in real time, leading to perception that operates in much the same way as a human brain.

One of the disruptive technology’s key advantages over traditional deep learning is a reduced reliance on expensive and often error-prone manually labeled training data sets. It is claimed that the unsupervised AI system successfully interprets and navigates unusual driving scenarios and edge cases where traditional supervised learning systems are least reliable.

Reduced reliance on stored data also means the system requires roughly 10 times less computing power than currently available systems and can be produced at lower cost, increasing the accessibility of ADAS across market segments at a time when regulations require more driver assistance capabilities for passenger and commercial vehicles.

“We are thrilled to partner with Continental to bring our revolutionary technology to the market,” said Igal Raichelgauz, CEO of AutoBrains. “Unsupervised AI is backed by more than 200 patents, over a decade of research and development, and a nearly two-year incubation period with Continental, and we are excited to take the next step with Conti as our key partner.”

The potential of the company’s technology has been recognized by Continental, as Frank Petznick, head of the driver assistance systems business unit at Continental, explained: “We are excited to be partnering with AutoBrains to bring to market its advanced and proven AI technology that we believe will disrupt the ADAS and AV marketplace. Historically, AV and ADAS technologies have been limited by their dependence on supervised learning that uses massive labeled training data sets and requires enormous compute power.

“AutoBrains’ AI breaks through those barriers with a different approach that processes relevant signals from the car’s environment in much the same way that human drivers do. This technology boosts performance while saving compute power and energy. With AutoBrains, we intend to push rapidly ahead toward a safer and increasingly autonomous driving experience.”

The post Unsupervised-AI developer gains backing from Continental for AD and ADAS development appeared first on Artificial Intelligence.

Python, the general-purpose coding language has gained much popularity over the years. Speaking of web development, app designing, scientific computing or machine learning, Python has it all. Due to this favourability of Python in the market, python developers are also in high demand. They are required to be competent and out of the box thinkers- undoubtedly a race to win.

Are you one of those python developers? Do you find yourself lagging behind in proving your reliability? Maybe you are going wrong with some of your skills. Never mind!

I’m here to tell you of those 8 trendsetting skills you need to hone. Implement them and prove your expertise in the programming world. Come, let’s take a look!

1.   Knowing Python Libraries

Being able to use the Python Library in its full potential also decides your expertise with this programming language. Python libraries like Panda, Matplotlib, Requests, Pyglet and more consist of reusable codes that you’d wish to add to your programs. These libraries are boon to you as a developer. They will increase workflow and make task execution way easier. Nothing saves more time from having to write the whole code every time.

2.  Knowing Python Frameworks

You might know how Python omits repeated code by using pre-developed frameworks. As a developer using a Python framework, you typically write code which conforms to some kind of conventions. Because of which it becomes easy to delegate responsibilities for the communications, infrastructure and low-level stuff to the framework. You can, therefore, concentrate on the logic of the application in your own code. If you have a good knack of these Python frameworks it can be a blessing, as it allows smooth flow of development. You may not know them all, but it’s advisable to keep up with some popular ones like Flask, Django and CherryPy.

Not sure of Python frameworks? You can seek help from Python Training Courses.

3.  Object Relational Mappers

Object-relational mapping (ORM) is a programming method used to access a database. It exposes your database into a series of objects without writing commands to insert or retrieve data. It may sound complex, but can save you a lot of time, and help to control access to your database. ORM tools can also be customised by a Python developer.

4.  Knowledge of Front End Technologies

Front end technologies like the HTML5, CSS3, and JavaScript will help you collaborate and work with a team of designers, marketers and other developers. Again, this can save a lot of development time.

5.  Analytical Skills

A good Python developer should have sharp analytical skills. You are expected to observe and critically come up with complex ideas, solutions or decisions about coding. Talking of the analytical skills in Python you need to have:

• A  complete idea of algorithms to write accurate codes.
• Visualize datasets, design attractive web sites for web development.
• Optimize algorithms

Analytical skills are a mark of your additional knowledge in the field. Building your analytical skills also make you a better problem solver.

6.  Data Science Foundation

Python Developers have a bright future in Data Science. Companies on the run will prefer developers with Data Science knowledge to create innovative tech solutions. Knowing Python will also gain your knowledge of probability, statistics, data wrangling and SQL. All of these are significant aspects of Data Science.

7.  Machine Learning and AI skill

Python is the right choice to grow in the Artificial Intelligence and Machine learning domain. It is an intuitive and minimalistic language with a full-featured library line (also called frameworks) which considerably reduces the time required to get your first results.

However, to master artificial intelligenceand  machine learning with Python you need to have a strong command over Python syntax. A fair grounding with calculus, data science and statistics can make you a pro. If you are a beginner, you can gain expertise in these areas by brushing up your math skills for Python Mathematical Libraries. Gradually, you can acquire your adequate Machine Learning skills by building simple neutral networks.

8.  Deep Learning

In the coming years, deep learning professionals will be well-positioned as there is a huge possibility awaiting in this field. With Python, you should be able to easily develop and evaluate deep learning models. Since deep learning is the advanced model of Machine Learning, to be able to bring it into complete functionality you should first get hands-on:  

• Artificial Neural Network Architecture
• Natural Language Processing (NLP)
• Audio and Video Processing
• A good python developer is also a mixture of several soft skills like proactivity, communication and time management. Most of all, a career as a Python Developer is challenging, but at the same time interesting. Empowering yourself with these skill sets is sure to take you a long way. Push yourself from the comfort zone and work hard from today!

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Solo.io has launched an initiative to define a format to extend the Open Container Initiative (OCI) image specification to standardize how an image bundles and stores metadata when developers write code using WebAssembly (WASM).

Company CEO Idit Levine says that as more organizations embrace WASM as a portable binary-code format for executable programs created using an assembly language, there needs to be a standard format for capturing metadata to promote interoperability across those projects.

WASM, in the case of Solo.io, is being employed to extend the capabilities of Envoy, open source proxy software being developed under the auspices of the Cloud Native Computing Foundation (CNCF). WASM is employed to add custom business logic and filters that extend the capabilities of Envoy to create such things as Solo Gloo, an open source application programming interface (API) gateway and ingress controller for Kubernetes environments. An implementation of WASM is also included as a sandbox-level project within the open source Istio service mesh, which is based on Envoy. WASM was created under the auspices of the World Wide Web Consortium (W3C) to provide a means for building applications that interoperate across multiple browsers.

WASM is now starting to be employed more broadly, but as it gains popularity Levine says more work needs to be done to prevent fragmentation. The format proposed by Solo.io is an outgrowth of the work the company put into developing WebAssembly Hub, a registry for building and sharing WebAssembly extensions such as filters for Envoy.

It’s still early days as far as the adoption of proxy servers and services meshes are concerned. However, as IT environments become more complex in the age of microservices, there is a need for higher levels of abstractions that mask the underlying IT infrastructure from developers. Rather than having to know how to call an API for a specific platform, a developer can request a service through a proxy server or service mesh. That approach ultimately makes networking and security agile enough to incorporate within a DevOps workflow.

Of course, most organizations will need to extend proxy software and service meshes across both monolithic and microservices-based applications. As that transition occurs, the convergence of network and DevOps operations will accelerate. IT teams that deploy microservices on top of Kubernetes clusters can expect to be at the forefront of that transition.

Naturally, the biggest issues most organizations will encounter as network operations and DevOps converge will be cultural. Most networking teams are just now making the transition to software-defined network overlays that allows them to more flexibly manage fleets of routers and switches. Most of those teams are managing those overlays using graphical tools versus programmatically managing them in the same way DevOps teams provision virtual machine resources. In many cases, the functions provided by an SDN today might simply become extensions of platforms such as Envoy and Istio tomorrow.

Regardless of approach, the days when networking services were surfaced manually by network operations teams using command-line interfaces (CLIs) on each network appliance are coming to a merciful end.

The post Solo.io Proposes OCI Format Extension for WASM appeared first on Artificial Intelligence.

Deepa Kalani and Ramiro Salas from the VMware team spoke at SpringOne 2020 Conference last week about the Tanzu Service Mesh (TSM) product and how it helps developers with Global Namespaces to implement access control and security policies, as well as visualization tools to show application-centric metrics.

A service mesh decouples services from having to know about the network and helps developers to focus on business logic. A typical service mesh can provide:

• Service discovery
• Weighted routing (for A/B deployments)
• Mutual TLS based authentication (including certificate rotation)
• Advanced telemetry for in-depth observability
• Fault injection and retries
• Circuit breakers

Today developers are working on cloud native applications and need to deal with monolithic apps, microservices, and serverless functions (FaaS) based models. A Service Mesh must be able to support all of the corporate developer use cases. It also needs to be transparent and it needs to be out of the way, so developers can be productive by focusing on application functionality and business logic.

Salas and Kalani also talked about VMware’s service mesh vision which incudes eliminating fragmentation by consistently connecting, controlling and monitoring software hosted on traditional VM’s as well as Kubernetes and public clouds. Tanzu Service Mesh consits of an SaaS based control plane that supports multi-cluster and multi-platform infrastructure.

Salas discussed Global Namespaces support in the service mesh, which helps with isolating and decoupling the applications from infrastructure. TSM also leverages sidecar technologies like Envoy that don’t need to add a lot of overhead and can reach out to the edge of the VM’s.

He also talked about some of the new projects they are working on, like Project Hamlet, an open-source initiative to define service discovery API for federated service meshes. The project supports federation with third party service meshes like Google Anthos or Hashicorp ConsulConnect.

Tanzu Service Mesh’s Policy Framework provides a continuous security model for cloud-native applications. Policy enforcement can be based on end-users, services/api, or the data. The framework includes an Identity Engine, Resource Grouping Model and a risk-based policy actions module.

Kalani demonstrated the different features of Tanzu Service Mesh and showed how to view the service graph to monitor the calls to each service. For more information about SpringOne Conference, checkout the conference main website and the schedule. The conference material (videos and slides) will be made available to the community starting this week.

The post VMware Tanzu Service Mesh from a Developer’s Perspective appeared first on Artificial Intelligence.

Companies across industries are exploring and implementing artificial intelligence (AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According to McKinsey, “embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth.” But with that promise comes challenges.

Computers and machines don’t come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

Data or bust

There are multiple ways to achieve AI, and existential to them all is data. Without quality data, artificial intelligence is a pipedream. There are two ways data can be manipulated—either through rules or machine learning—to achieve AI, and some best practices to help you choose between the two methods.

Rules-based systems

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems as rules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of “If” statements (“if A, then do B, else if X, then do Y”).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules won’t change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what they’ve been written to do.

Machine learning systems

While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that aren’t working anymore.

In practice, there are several ways a machine can learn, but supervised training—when the machine is given data to train on—is generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

Where to start with an organization’s AI strategy:

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve. What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a company’s AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

• Fixed outcomes: When there is a small or fixed number of outcomes. For example, there are only two states for which an “Add to Cart” button can be in, either pressed or not. While it is possible to use machine learning to detect whether a user pressed the button, it wouldn’t make sense to apply that type of method.
• Risk of error: The penalty of error is too high to risk false positives and therefore only rules—which will be 100 percent accurate—should be implemented.
• Not planning for ML: If those maintaining the system don’t have machine learning knowledge and the business does not have plans to source for it moving forward.

When to apply machine learning:

• Simple rules don’t apply: When there is no easily definable way to solve a task using simple rules
• Speed of change: When situations, scenarios, and data are changing faster than the ability to continually write new rules.
• Natural language processing: Tasks that call for an understanding of language, or natural language processing. Since there are an infinite number of ways to say something, it’s unrealistic, if not downright impossible, to write rules for normal language. The innate, adaptive intelligence of machine learning is optimized for scale.

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

The post How to choose between rule-based AI and machine learning appeared first on Artificial Intelligence.

Over the past few years, articles and blog posts have started to ask some version of the same question: “Why are all websites starting to look the same?”

These posts usually point out some common design elements, from large images with superimposed text, to hamburger menus, which are those three horizontal lines that, when clicked, reveal a list of page options to choose from.

My colleagues Bardia Doosti, David Crandall, Norman Su and I were studying the history of the web when we started to notice these posts cropping up. None of the authors had done any sort of empirical study, though. It was more of a hunch they had.

We decided to investigate the claim to see if there were any truth to the notion that websites are starting to look the same and, if so, explore why this has been happening. So we ran a series of data mining studies that scrutinized nearly 200,000 images across 10,000 websites.

How do you even measure similarity?

It’s virtually impossible to study the entire internet; there are over a billion websites, with many times as many webpages. Since there’s no list of them all to choose from, performing a random sample of the internet is off the table. Even if it were possible, most people only see a tiny fraction of those websites regularly, so a random sample may not even capture the internet that most people experience.

We ended up using the websites of the Russell 1000, the top U.S. businesses by market capitalization, which we hoped would be representative of trends in mainstream, corporate web design. We also studied two other sets of sites, one with Alexa’s 500 most trafficked sites, and another with sites nominated for Webby Awards.

Because we were interested in the visual elements of these websites, as data, we used images of their web pages from the Internet Archive, which regularly preserves websites. And since we wanted to gather quantitative data comparing millions of website pairs, we needed to automate the analysis process.

To do that, we had to settle on a definition of “similarity” that we could measure automatically. We investigated both specific attributes like color and layout, as well as attributes learned automatically from data using artificial intelligence.

For the color and layout attributes, we measured how many pixel-by-pixel edits we would have to make to transform the color scheme or page structure of one website into another. For the AI-generated attributes, we trained a machine learning model to classify images based on which website they came from and measure the attributes the model learned. Our previous work indicates that this does a reasonably good job at measuring stylistic similarity, but it’s very difficult for humans to understand what attributes the model focused on.

How has the internet changed?

We found that across all three metrics—color, layout and AI-generated attributes—the average differences between websites peaked between 2008 and 2010 and then decreased between 2010 and 2016. Layout differences decreased the most, declining over 30% in that time frame.

These findings confirm the suspicions of web design bloggers that websites are becoming more similar. After showing this trend, we wanted to study our data to see what kinds of specific changes were causing it.

You might think that these sites are simply copying each other’s code, but code similarity has actually significantly decreased over time. However, the use of software libraries has increased a lot.

Libraries feature collections of generic code for common tasks, like resizing a page for mobile devices or making a hamburger menu slide in and out. We looked at which sites had lots of libraries in common and how similar they looked. Sites built with certain libraries—Bootstrap, FontAwesome and JQuery UI—tended to look much more similar to each other. This could be because these libraries control page layout and have commonly used default options. Sites that used other libraries, like SWFObject and JQuery Tools, tended look much different, and that might be due to that fact that those libraries allow for more complex, customized pages.

The changes of websites from 2005 to 2016 illustrate what’s happening.

Sites with average similarity scores in 2005 tended to look less similar than those with average similarity scores in 2016.

For example, in 2005, Webshots.com and Yum.com were considered relatively similar, but had somewhat different color schemes and very different layouts. While they both mostly use white, blue and black, the site on the right has a blue background.

Two 2016 sites, Xfinity.com and Gilt.com, on the other hand, are even more similar: They both have a menu bar on the top and are primarily white and black with images. These pages have much less text and make better use of the higher resolution monitors that exist now.

Is conformity healthy?

What should be made of this creeping conformity?

On the one hand, adhering to trends is totally normal in other realms of design, like fashion or architecture. And if designs are becoming more similar because they’re using the same libraries, that means they’re likely becoming more accessible to the visually impaired, since popular libraries are generally better at conforming to accessibility standards than individual developers. They’re also more user-friendly, since new visitors won’t have to spend as much time learning how to navigate the site’s pages.

On the other hand, the internet is a shared cultural artifact, and its distributed, decentralized nature is what makes it unique. As home pages and fully customizable platforms like NeoPets and MySpace fade into memory, web design may lose much of its power as a form of creative expression. The Mozilla Foundation has argued that consolidation is bad for the “health” of the internet, and the aesthetics of the web could be seen as one element of its well-being.

And if sites are looking more similar because many people are using the same libraries, the large tech companies who maintain those libraries may be gaining a disproportionate power over the visual aesthetics of the internet. While publishing libraries that anyone can use is likely a net benefit for the web over keeping code secret, big tech companies’ design principles are not necessarily right for every site.

This outsize power is part a larger story of consolidation in the tech industry—one that certainly could be a cause for concern. We believe aesthetic consolidation should be critically examined as well.

The post Yes, websites really are starting to look more similar appeared first on Artificial Intelligence.

Thanks to smartphones and their downsized keyboards, autocomplete has become a nearly ubiquitous feature of how we write these days. To save us precious seconds composing and (at least in my fat-thumbed case) correcting words, our keyboards now prompt us with suggestions of what we’re trying to write to get the job done a little bit more easily. But email and messaging composing isn’t the only area where artificial intelligence and semantic analytics are being used in this way. Today, a startup that has built a platform that applies the concept to the world of coding is announcing a round of funding to expand its business.

Codota, an Israeli startup that provides an AI tool to developers to let them autocomplete strings of code that they are writing — intended both to speed up their work (it claims to “boost productivity by 25%”) and to make sure that it’s using the right syntax and ‘spelled’ correctly — has picked up $12 million, a Series A led by e.ventures, with participation also from previous backer Khosla Ventures, along with new investors TPY Capital and Hetz Ventures. The company has now raised $16 million in total, and it’s not disclosing its valuation.

The funding comes on the heels of Codota  acquiring one of its bigger competitors, TabNine, out of Canada, late last year (announced only in March however) to expand the number of languages that it can support. It now says it supports all major languages, including Python, JavaScript, Java, C, and HTML; and it operates across a major integrated development environments such as VSCode, Eclipse, and IntelliJ.

The funding will be used to expand its reach into that existing range further, as well as to bring on more customers. Today, the list of those that are already using Codota’s tools is impressive. It includes developers from companies like Google and Amazon, as well as Netflix, Alibaba, Airbnb and Atlassian, amongst many others. It says its user base has grown more than 1,000% in the last year, number over 1 million developers using it monthly.

The funding news coincides with Codota launching a new version of its autocompletion for JavaScript that merges Codota’s semantic technology with TabNine’s textual tech.

The first two names in the customer list above list are particularly surprising, but they underscore what Codota has focused on and seems to be doing right. These two tech giants are AI powerhouses in their own rights; both build and ship formidable sets of tools for developers; and Google specifically is one of the names most synonymous with autocomplete by way of the tools it’s built for Gmail.

The reason Codota — which was founded in 2015 — has had traction, said co-founder and CTO Eran Yahav, is because in fact coding has been a tough nut to crack for semantics teams, despite their advances in other languages.

“Up until a couple of years ago it wasn’t feasible,” he said, noting that four streams of technology are coming together when building auto-completion for coding: high-quality open source code availability to feed the algorithms; advances in semantic analysis to extract insights at scale; machine learning advances that essentially bring ML costs down; and computational resources to run everything in the cloud to make it something everyone can use everywhere. With open source really booming, and everything else coming along, it’s been a perfect storm that Codota has seized, even as others are working on this too.

“Others have done this with varying degrees of success,” Dror Weiss, the other co-founder and CEO said. “I’m guessing and also know that others are doing the same.” Others include the likes of Kite, Ubisoft and Mozilla, and a number of others.

One aspect that Codota has been building that is particularly timely now is the ability not just to provide more precise coding assistance to developers, but to “learn” what is best practice in a particular environment or workplace (it offers both individual and enterprise tiers, and it’s the latter that provides this feature). This can be useful in any situation, but particularly today when developers are working at home and on their own, giving them instant help as if they were physically in the same space.

Notably, while so much AI seems bent on the idea of autonomous systems, Weiss said emphatically that this is not the intention short or even long-term.

“I don’t think we would be able to replace developers and I don’t think we would want to,” he said. “Our aim is to take the mundane and repetitive aspects and do that for them.” In that respect not unlike RPA in back-office functions. “It’s not high value to remember the syntax and the best practice. And even if you have smart compose [in consumer services] it may suggest sentences but can’t read your mind and tell you how you want to respond. So it’s very unlikely to replace you and respond how you would want to. That’s not even in our longest-term plan.”

e.ventures  last year announced a $400 million fund for early-stage investments, and this seems to be coming out of that. With this round, the firm’s general partner Tom Gieselmann is joining the board.

“I’ve been following the developer tools market for over 20 years and believe Codota has distinguished itself as the dominant player in terms of community, product, and technology,” he said in a statement. “We are proud to support Dror and Eran on their mission to transform software development and make coding easier and more efficient for individual developers and teams within the enterprise.”

The post Codota picks up $12M for an AI platform that auto-completes developers’ code appeared first on Artificial Intelligence.

AI Needs To Be On the Edge

In the age of innovation, as data continues to grow, there’s a real need for data storage and data computation to be located on the device. Privacy, security, and speed are the biggest reasons that the distributed Edge model can work better in certain use-cases. When we are all concerned about our personal data being stored in a cloud server, what if the app can handle our personal data on device instead. With Edge AI, personalization features that we want from the app can be achieved on device. Transferring data over networks and into cloud-based servers allows for latency. At each endpoint, there are security risks involved in the data transfer.

While cloud computing offers unquestionable economies of scale, a distributed computing model is driven by the nature of the data itself. The volume of data will make it difficult or expensive to move due to bandwidth costs or availability. The velocity of data will catalyze more real-time applications that cannot be limited by network latency. And the variety of data will be governed by regulatory, privacy and security constraints.Jonathan Ballon, Vice President and General Manager, Internet of Things Group, Intel.

This is why the Edge AI Software market is forecasted to grow from $355 million in 2018 to 1.12 trillion dollars by 2023.Today In: AI

Edge AI Training Is An Opportunity for All Types of Developers

At the beginning of the AI evolution, we were concerned with crossing over from statistical models to data science, machine learning, and building algorithms that run on the Cloud. Now, software engineers increasingly find that their projects are naturally scoped to include an AI component. You don’t have to be a machine learning engineer to know about deep learning or reinforcement learning.

PROMOTED

Now, IoT developers who may have been sitting on the sidelines working on software projects that are more feature-based than data-based, will have an opportunity to get involved in the AI evolution. The Intel® Edge AI for IoT Developers Nanodegree Program will introduce students to the Intel® OpenVINO™ toolkit that will allow developers to deploy pre-trained deep learning models through a high-level C++ or Python inference engine AP integrated with application logic.

Students will work on Intel’s IoT DevCloud to develop, test, and run their workloads on a cluster of the latest Intel hardware and software. Not only will IoT developers learn to apply AI in their applications, but they will also work on performance and other issues that arise with building data-centric applications.

Software engineers, machine learning engineers, data scientists and other technologists who’ve been working on Cloud-based AI applications now have a new direction to take in their learning path. Learning to develop Edge AI applications can allow a new perspective toward more user-driven application development of AI. With more user-based personalizations directly on the device, business solutions, features, and user data can be viewed from a more user-centric perspective.

This program will be beneficial for all developers who want to be involved in AI-based projects.

The Intel and Udacity Collaboration

This Intel and Udacity collaboration will be the pioneer program that will lay the foundation of training in Edge AI for the next years to come. Just like Udacity’s hugely popular machine learning and AI Nanodegree programs, Intel® Edge AI for IoT Developers Nanodegree Program will facilitate streamlined training, project-based learning, mentorship and certification that will allow for a quick ramp-up of both AI and IoT development knowledge.

For computer science majors fresh out of school looking for an entry point into the industry, this type of program can offer both opportunities and skills that bridge the gap between education and real-world applications.

The Vision of Edge AI

At a time when we are all concerned about our job security and prospects due to the coronavirus pandemic, it’s good to know that there are new paths to explore. If you don’t currently work in manufacturing or healthcare, it’s difficult to envision Edge AI used on factory assembly lines, and in Urgent Care medical imaging equipment.

But, how about imagining the technology in drones, security cameras, robots and self-driving cars?

There are many smart phone apps that we use day to day that can potentially deliver more personalized features through an AI component.

The value and impact of Edge AI applications is showing no limits when it comes to use cases. The ingenuity of companies during this COVID-19 crisis is humbling. Industries including public safety and healthcare, for example are designing and deploying solutions now that leverage AI and computer vision technologies to deliver accurate and real-time insights to help with tracking, testing and treatment. The technology exists, we are only limited by the imagination at scale of the developer community.Jonathan Ballon, Vice President and General Manager, Internet of Things Group, Intel.

This Edge AI evolution is the next generation of AI evolution that will change the way that we interact with our devices and offer better and more secure ways to deploy AI applications.

Projects And Mentors

Students from the program will learn directly from experienced professionals in the Edge AI and IoT field such as Stewart Christie, who has been with Intel for almost 20 years, and is currently the Community Manager of the Internet of Things Developer Program; Archana Iyer, former Research Engineer at Saama; Soham Chatterjee, former Software Innovator at Intel; and Michel Virgo, Senior Curriculum Manager at Udacity.

The Project-based approach that Udacity uses will allows students to learn hands-on skills while interacting and receiving mentorships from experienced professionals. Many developers are familiar with this type of quick ramp up of development skillsets across multiple areas.

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