Some artists, developers, writers, and other creators have expressed concern that generative AIs may pose a threat to their livelihoods. However, an increasing number view such AIs as assistive tools that will help creators rather than replace them.

Stack Overflow surveyed its developer community to find out how developers feel about technologies currently making the headlines.

“Our latest pulse survey asked developers to think about nascent trends in technology and tell us how they felt about them,” explained Erin Yepis Senior Analyst, Market Research and Insights at Stack Overflow.

“With AI-assisted technologies in the news, this survey’s aim was to get a baseline for perceived utility and impact of a range of buzzworthy technologies in order to better understand the overall ecosystem.”

The technologies were ranked on a scale of zero (negative impact) to 10 (positive impact) based on expected world impact:

“Possibly what we are seeing here as far as why developers would not rate AI more negatively than technologies like low code/no code or blockchain but do give it a higher emergent score is that they understand the technology better than a typical journalist or think tank analyst,” reflects Yepis.

“Developers understand the distinction between media buzz around AI replacing humans in well-paying jobs and the possibility of humans in better quality jobs when AI and machine learning technologies mature.”

Machine learning (18%) and AI-assisted technologies (13%) came out on top of the technologies that developers want more hands-on training with. Despite ranking just about over the mean in expected world impact, blockchain (9%) took third place. Cloud computing (8%) and open-source (5%) took the fourth and fifth top spots, respectively.

The headline-grabbing technologies were also ranked on a scale of zero (experimental) to 10 (proven) based on how ready for primetime developers view each as being:

There are few surprises here but some may find it interesting to see low-code/no-code being viewed as less ready for primetime than AI-assisted technologies. Apple will also be hoping that developers’ views around AR/VR will change after the widely-expected launch of its headset later this year.

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The company says that existing datasets of “virtually any shape and size” can be used for custom models.

A single command in the OpenAI command-line tool, alongside a user-provided file, is all that it takes to begin training. The custom GPT-3 model will then be available for use in OpenAI’s API immediately.

One customer says that it was able to increase correct outputs from 83 percent to 95 percent through fine-tuning. Another client reduced error rates by 50 percent.

Andreas Stuhlmüller, Co-Founder of Elicit, said:

“Since we started integrating fine-tuning into Elicit, for tasks with 500+ training examples, we’ve found that fine-tuning usually results in better speed and quality at a lower cost than few-shot learning.

This has been essential for making Elicit responsive at the same time as increasing its accuracy at summarising complex research statements.

As far as we can tell, this wouldn’t have been doable without fine-tuning GPT-3”

Joel Hellermark, CEO of Sana Labs, commented:

“With OpenAI’s customised models, fine-tuned on our data, Sana’s question and content generation went from grammatically correct but general responses to highly accurate semantic outputs which are relevant to the key learnings.

This yielded a 60 percent improvement when compared to non-custom models, enabling fundamentally more personalised and effective experiences for our learners.”

In June, Gartner said that 80 percent of technology products and services will be built by those who are not technology professionals by 2024. OpenAI is enabling custom AI models to be easily created to unlock the full potential of such products and services.

Related: OpenAI removes GPT-3 API waitlist and opens applications for all developers

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The company’s roster of accelerated computing kits now exceeds 150 and supports the almost three million developers in NVIDIA’s Developer Program.

Four of the major new SDKs are:

• ReOpt – Automatically optimises logistical processes using advanced, parallel algorithms. This includes vehicle routes, warehouse selection, and fleet mix. The dynamic rerouting capabilities – shown in an on-stage demo – can reduce travel time, save fuel costs, and minimise idle periods.
• cuNumeric – Implements the popular NumPy application programming interface and enables scaling to multi-GPU and multi-node systems with zero code changes.
• cuQuantum – Designed for quantum computing, it enables large quantum circuits to be simulated faster. This enables quantum researchers to simulate areas such as near-term variational quantum algorithms for molecules, error correction algorithms to identify fault tolerance, and accelerate popular quantum simulators from Atos, Google, and IBM.
• CUDA-X accelerated DGL container – Helps developers and data scientists working on graph neural networks to quickly set up a working environment. The container makes it easy to work in an integrated, GPU-accelerated GNN environment combining DGL and Pytorch.

Some existing AI-related SDKs that have received notable updates are:

• Deepstream 6.0 – introduces a new graph composer that makes computer vision accessible with a visual drag-and-drop interface.
• Triton 2.15, TensorRT 8.2 and cuDNN 8.4 – assists with the development of deep neural networks by providing new optimisations for large language models and inference acceleration for gradient-boosted decision trees and random forests.
• Merlin 0.8 – boosts recommendation systems with its new capabilities for predicting a user’s next action with little or no user data and support for models larger than GPU memory.

Accelerating quantum research

Nvidia has established a partnership with Google, IBM, and a number of small companies, national labs, and university research groups to accelerate quantum research.

“It takes a village to nurture an emerging technology, so Nvidia is collaborating with Google Quantum AI, IBM, and others to take quantum computing to the next level,” explained the company in a blog post.

The first library from the aforementioned new cuQuantum SDK is Nvidia’s initial contribution to the partnership. The library is called cuStateVec and is an accelerator for the state vector simulation method which tracks the full state of the system in memory and can scale to tens of qubits.

cuStateVec has been integrated into Google Quantum AI’s state vector simulator qsim and can be used through the open-source framework Cirq.

“Quantum computing promises to solve tough challenges in computing that are beyond the reach of traditional systems,” commented Catherine Vollgraff Heidweiller at Google Quantum AI.

“This high-performance simulation stack will accelerate the work of researchers around the world who are developing algorithms and applications for quantum computers.”

In December, cuStateVec will also be integrated with Qiskit Aer—a high-performance simulator framework for quantum circuits from IBM.

Among the national labs using cuQuantum to accelerate their research are Oak Ridge, Argonne, Lawrence Berkeley National Laboratory, and Pacific Northwest National Laboratory. University research groups include those at Caltech, Oxford, and MIT.

Nvidia is helping developers to get started by creating a ‘DGX quantum appliance’ that puts its simulation software in a container optimised for its DGX A100 systems. The software will be available early next year via the company’s NGC Catalog.

(Image Credit: Nvidia)

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Video games have pioneered AI developments since Nim was released in 1951. In the decades since, game developers have worked to improve AIs to provide a more enjoyable experience for a growing number of people around the world.

Just imagine the horror if those developers found out their work was instead being used for real military purposes without their knowledge. That’s exactly what developers behind the popular Unity game engine discovered.

According to a Vice report, three former and current Unity employees confirmed that much of the company’s contract work is to do with AI programming. That’s of little surprise and wouldn’t be of too much concern if it wasn’t conducted under the “GovTech” department with seemingly a high degree of secrecy.

“It should be very clear when people are stepping into the military initiative part of Unity,” one of Vice’s sources said, on condition of anonymity for fear of reprisal.

Vice discovered several deals with the Department of Defense, including two six-figure contracts for “modeling and simulation prototypes” with the US Air Force.

Unity bosses clearly understand that some employees may not be entirely comfortable with knowing their work could be used for war. One memo instructs managers to use the terms “government” or “defense” instead of “military.”

In an internal Slack group, Unity CEO John Riccitello promised to have a meeting with employees.

“Whether or not I’m working directly for the government team, I’m empowering the products they’re selling,” wrote Riccitello. “Do you want to use your tools to catch bad guys?”

That question is likely to receive some passionate responses. After all, few of us are going to forget the backlash and subsequent resignation of Googlers following revelations about the company’s since-revoked ‘Project Maven’ contract with the Pentagon.

You can find Vice’s full report here.

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Copilot draws context from the code that a developer is working on and can suggest entire lines or functions. The system, from OpenAI, claims to be “significantly more capable than GPT-3” in generating code and can help even veteran programmers to discover new APIs or ways to solve problems.

Critics claim the system is using copyrighted code that GitHub then plans to charge for:

Julia Reda, a researcher and former MEP, published a blog post arguing that “GitHub Copilot is not infringing your copyright”.

GitHub – and therefore its owner, Microsoft – is using the huge number of repositories it hosts with ‘copyleft’ licenses for its tool. Copyleft allows open-source software or documentation to be modified and distributed back to the community.

Reda argues in her post that clamping down on tools such as GitHub’s through tighter copyright laws would harm copyleft and the benefits it offers.

One commenter isn’t entirely convinced:

“Lots of people have demonstrated that it pretty much regurgitates code verbatim from codebases with abandon. Putting GPL code inside a neural network does not remove the license if the output is the same as the input.

A large portion of what Copilot outputs is already full of copyright/license violations, even without extensions.”

Because the code is machine-generated, Reda also claims that it cannot be determined to be ‘derivative work’ that would face the wrath of intellectual property laws.

“Copyright law has only ever applied to intellectual creations – where there is no creator, there is no work,” says Reda. “This means that machine-generated code like that of GitHub Copilot is not a work under copyright law at all, so it is not a derivative work either.”

There is, of course, also a debate over whether the increasing amounts of machine-generated work should be covered under IP laws. We’ll let you decide your own position on the matter.

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Vertex was announced during this year’s virtual I/O developer conference and somewhat breaks from Google’s tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers.

Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms.

Bradley Shimmin, Chief Analyst for AI Platforms, Analytics, and Data Management at Omdia, said:

“Data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order.

It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”

Vertex brings together Google Cloud’s AI solutions into a single environment where models can go from experimentation all the way to production.

Andrew Moore, VP and GM of Cloud AI and Industry Solutions at Google Cloud, said:

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Vertex provides access to Google’s MLOps toolkit which the company uses internally for workloads involving computer vision, conversation, and language.

Other MLOps features supported by Vertex include Vizier, which increases the rate of experimentation; Feature Store to help practitioners serve, share, and reuse ML features; and Experiments to accelerate the deployment of models into production with faster model selection.

Some high-profile companies were given early access to Vertex. Among them is ModiFace, a part of L’Oréal that focuses on the use of AR and AI to revolutionise the beauty industry.

Jeff Houghton, COO at ModiFace, said:

“We provide an immersive and personalized experience for people to purchase with confidence whether it’s a virtual try-on at web check out, or helping to understand what brand product is right for each individual.

With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life.”

ModiFace uses Vertex to train AI models for all of its new services. For example, the company’s skin diagnostic service is trained on thousands of images from L’Oréal’s Research & Innovation arm and is combined with ModiFace’s AI algorithm to create tailor-made skincare routines.

Another firm that is benefiting from Vertex’s capabilities is Essence, a media agency that is part of London-based global advertising and communications giant WPP.

With Vertex AI, Essence’s developers and data analysts are able to regularly update models to keep pace with the rapidly-changing world of human behaviours and channel content.

Those are just two examples of companies whose operations are already being greatly enhanced through the use of Vertex. Now the floodgates have been opened, we’re sure there’ll be many more stories over the coming years and we can’t wait to hear about them.

You can learn how to get started with Vertex AI here.

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While still technically in pre-release, the Mac-optimised TensorFlow fork supports native hardware acceleration on Mac devices with M1 or Intel chips through Apple’s ML Compute framework.

The new TensorFlow release boasts of an over 10x speed improvement for common training tasks. While impressive, it has to be taken in the context that the GPU was not previously used for training tasks. 

A look at the benchmarks still indicates a substantial gap between the Intel and M1-based Macs across various machine learning models:

In a blog post, Pankaj Kanwar, Tensor Processing Units Technical Program Manager at Google, and Fred Alcober, TensorFlow Product Marketing Lead at Google, wrote:

“These improvements, combined with the ability of Apple developers being able to execute TensorFlow on iOS through TensorFlow Lite, continue to showcase TensorFlow’s breadth and depth in supporting high-performance ML execution on Apple hardware.”

We can only hope that running these workloads doesn’t turn MacBooks into expensive frying pans—but the remarkable efficiency they’ve displayed so far gives little cause for concern.

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The cloud-based solution aims to help accelerate the delivery of software in a rapidly-changing world while maintaining a high bar of quality.

Gareth Smith, CTO of Eggplant, said:

“The launch of our cloud platform is a significant milestone in our mission to rid the world of bad software. In our new normal, delivering speed and agility at scale has never been more critical.

Every business can easily tap into Eggplants’ AI-powered automation platform to accelerate the pace of delivery while ensuring a high-quality digital experience.” 

Enterprises have accelerated their shift to the cloud due to the pandemic and resulting increases in things such as home working.

Recent research from Centrify found that 51 percent of businesses which embraced a cloud-first model were able to handle the challenges presented by COVID-19 far more effectively.

Eggplant’s Digital Automation Intelligence (DAI) Platform features:

• Cloud-based end-to-end automation: The scalable fusion engine provides frictionless and efficient continuous and parallel end-to-end testing in the cloud, for any apps and websites, and on any target platforms. 
• Monitoring insights: The addition of advanced user experience (UX) data points and metrics, enables customers to benchmark their applications UX performance. These insights, added to the UX behaviour helps improve SEO. 
• Fully automated self-healing test assets: The use of AI identifies the tests needed and builds and runs them automatically, under full user control. These tests are self-healing, and automatically adapt as the system-under-test evolves.   

The solution helps to support the “citizen developer” movement—using AI to enable no-code/low-code development for people with minimal programming knowledge.

Both cloud and AI ranked highly in a recent study (PDF) by Deloitte of the most relevant technologies “to operate in the new normal”. Cloud and cybersecurity were joint first with 80 percent of respondents, followed by cognitive and AI tools (73%) and the IoT (65%).

Eggplant’s combination of AI and cloud technologies should help businesses to deal with COVID-19’s unique challenges and beyond.

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The new video courses assume the developer already has a basic standard of Python skills. If you don’t, I’m afraid you’ll need to brush up on those first. Fortunately, Microsoft released a 44-part “Python for Beginners” series last autumn (or “fall” for our American friends.)

For those with the Python skills, or if you’ve just consumed all 44-parts of Microsoft’s course in record time, the new courses are around three hours each. 

The first course, More Python for Beginners, features 20 videos and covers areas such as lambdas, inheritance, and asynchronous operations.

The second course, Even More Python for Beginners: Data Tools, consists of 31 videos and really dives into using Python for machine learning and data science. Students are taught how to use popular Python libraries for the aforementioned topics; along with using the Jupyter Notebooks browser-based development environment.

Each of the courses are still led by Christopher Harrison, senior program manager at Microsoft, and Susan Ibach, business development manager from Microsoft AI Gaming. 

“While we’re not going to get into conversations about choosing algorithms or building models, we are going to introduce what you’ll use when you begin the journey. We’ll highlight Jupyter Notebooks, the favorite tool of data scientists,” Harrison and Ibach wrote in a blog. 

As of writing, the first part in Microsoft’s Python course series has been viewed over 1.75 million times.

The course’s popularity is of little surprise given the huge interest in Python as AI talent becomes more in-demand; with six-figure salaries not unheard of. In last year’s GitHub Octoverse report, Python overtook Java to become the second most popular language on the world’s largest repository host.

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Edison is an AI platform launched last year to help with leveraging data from imaging devices.

Kieran Murphy, President and CEO at GE Healthcare, says:

“We introduced Edison just one year ago at RSNA to help health providers take advantage of data in new and significant ways.

With the introduction of the Edison Developer Program, and a suite of new intelligent applications and smart devices powered by Edison, we are building on that promise as we continue to work with partners to realize our collective goal of advancing the future of health.”

While there’s significant interest in using AI for healthcare, the time it takes for implementing new innovations is “cumbersome and complex,” according to GE Healthcare.

GE Healthcare aims to serve clinicians’ need for a single solution to assist with integrating AI algorithms into existing workflows to help them, and therefore their patients, benefit from the potential of these new technologies much faster.

Market-ready AI applications will be deeply integrated into GE Healthcare’s vast existing solutions – on medical devices, in the cloud, or at the edge of the network.

Developers jumping aboard the new initiative will have the reach of GE Healthcare’s large userbase. GE Healthcare’s business reported $19 billion in revenue last year and spans 160 countries. On average, three patients are imaged with the company’s solutions every second.

“The Edison Developer Program exposes a number of potential capabilities of the Edison platform, including secure device connectivity, data aggregation for clinical context, advanced visualization, workflow and AI orchestration, in addition to a rich set of AI capabilities for data traceability, curation, annotation, model training and inferencing,” the company wrote in a press release.

“This set of services will reduce the complexity of developing and integrating AI and data-based healthcare applications in clinical workflows. Program members are selected and vetted based on rigorous clinical and technical evaluations as well as regulatory clearance to ensure confidence and security of solutions offered through the Edison platform.”

GE Healthcare is currently working with a range of AI and analytics companies including Arterys, iCAD, Koios Medical, MaxQ AI and Volpara.

Koios Medical, for example, built and deployed Breast Assistant – an AI-based risk assessment for breast cancer that aligns to a BI-RADS (Breast Imaging-Reporting and Data System) category.

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