The New York Times claims ChatGPT set off alarm bells at Google. At the invite of Google CEO Sundar Pichai, the company’s founders – Larry Page and Sergey Brin – returned for a series of meetings to review Google’s AI product strategy.

Google is one of the biggest investors in AI and has some of the most talented minds in the industry. As a result, the company is scrutinised more than most when it comes to any AI developments.

In 2020, leading AI ethics researcher Timnit Gebru was fired by Google. Gebru claims she was fired over an unpublished paper and sending an email critical of the company’s practices. Numerous other AI experts at Google left following her firing.

Just two years earlier, over 4,000 Googlers signed a petition demanding that Google cease its plans to develop AI for the US military. Google withdrew from the contract but not before at least a dozen employees resigned.

With the company in the spotlight, Google has allegedly been ultra-cautious in how it develops and deploys AI.

According to a CNBC report, Pichai and Google AI Chief Jeff Dean were asked in a meeting whether ChatGPT represented a “missed opportunity” for the company. Pichai and Dean said that Google’s own models were just as capable but the company had to move “more conservatively than a small startup” because of the “reputational risk” it poses.

Microsoft has invested so heavily in OpenAI that it’s hard to consider the company a small startup anymore. The two companies have established a deep partnership and Microsoft has begun integrating OpenAI’s technologies into its own products.

Earlier this month, AI News reported that Microsoft and OpenAI are set to integrate technology from OpenAI in Bing to challenge Google’s search dominance. That appears to have been what really set off the alarm bells at Google.

Google now appears to be speeding up the reveal and deployment of its own AI solutions. To that end, the company is reportedly working to speed up the review process which checks if it’s operating ethically.

One of the first AI solutions set to debut sounds very similar to what Microsoft and OpenAI have planned for Bing.

A demo of a chatbot-enhanced Google Search is expected at the company’s annual I/O developer conference in May. The demo will prioritise “getting facts right, ensuring safety and getting rid of misinformation.”

Other AI-powered product launches expected to be shown include an image generator, a set of tools for enterprises to develop their own AI prototypes within a browser window, and an app for testing such prototypes.

Google is also said to be working on a rival to GitHub Copilot, a coding assistant powered by OpenAI’s technology. Google’s alternative is called PaLM-Coder 2 and will have a version for building smartphone apps called Colab that will be integrated into Android Studio.

Overall, Google is set to unveil more than 20 AI-powered projects this year. The announcements should calm investors who’ve criticised Google’s slow AI developments in recent years but ethicists will be concerned about the company prioritising speed over safety.

(Photo by Mitchell Luo on Unsplash)

Relevant: OpenAI CEO: People are ‘begging to be disappointed’ about GPT-4

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The AI giant unveiled GPT-3 in May last year to a mixed reception. Few doubted GPT-3’s impressive ability to generate text similar to a human writer, but many expressed concerns about the societal impact.

Fake news and propaganda are already difficult to counter even when it’s being generated in relatively limited amounts by human writers. The ability for anyone to use an AI to generate misinformation at scale could have serious implications.

A paper (PDF) from the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism found that GPT-3 is able to generate “influential” text that has the potential to radicalise people into far-right extremist ideologies.

OpenAI itself shared those concerns and decided against releasing GPT-3 to the public at the time. Instead, only select trusted researchers and developers were given access.

The company gradually provided access to GPT-3 to more developers through a waitlist. OpenAI says “tens of thousands” of developers are already taking advantage of powerful AI models through its platform.

However, OpenAI has also been building a number of “safeguards” that have made the company feel comfortable removing the waitlist.

“Instruct” models are designed to adhere better to human instructions, provide specialised endpoints for more truthful question-answering, and deliver a free content filter to help developers mitigate abuse.

“To ensure API-backed applications are built responsibly, we provide tools and help developers use best practices so they can bring their applications to production quickly and safely,” wrote OpenAI in a blog post.

“As our systems evolve and we work to improve the capabilities of our safeguards, we expect to continue streamlining the process for developers, refining our usage guidelines, and allowing even more use cases over time.”

OpenAI has improved ‘Playground’ to make it even simpler for researchers to prototype with its models.

The company has also added an example library with dozens of prompts to get developers started. Codex, OpenAI’s new model for translating natural language into code, also makes an appearance.

Developers in supported countries can sign up and get started experimenting with OpenAI’s API right now.

19/11 update: An earlier version of the headline said that API was “generally” available. This has now been updated to clarify that an application process is still in place and that usage will still be reviewed by OpenAI.

(Photo by Dima Pechurin on Unsplash)

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DeepCode’s machine learning-based bot is fluent in JavaScript, TypeScript, Java, C/C++, and Python.

“Our data shows that over 50% of repositories have critical issues and every second pull-request has warnings about issues that need to be fixed,” said Boris Paskalev, CEO and co-founder of DeepCode.

“By using DeepCode, these issues are automatically identified and logically explained as suggestions are made about how to fix them before code is deployed.”

Over the past few months, DeepCode has focused on improving the JavaScript skills of the bot. JavaScript frameworks and libraries such as Vue.js and React are supported. A demo of DeepCode’s analysis of React can be found here.

DeepCode claims its bot is now “up to 50x faster and finding more than double the number of serious bugs over all other tools combined while maintaining over 80% accuracy.”

The bot has been trained using machine learning to analyse hundreds of millions of commits across the vast number of open source projects freely available. DeepCode says it’s able to identify bugs before they happen.

A recent survey by DeepCode found that 85 percent of people want software companies to focus less on new features and more on fixing bugs and security issues.

“Too many software companies still believe that new features are what users want the most,” commented Paskalev. “As this survey shows, what people really want is quality software that is safe to use.”

DeepCode is free for open source software and commercial teams of up to 30 developers. You can start analysing your code by connecting your GitHub, BitBucket, or GitLab account here.

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For AI enthusiasts, speculating about how artificial intelligence can improve software development is exciting. Will AI help create prototypes in days, instead of months or years? Will it teach human developers how to code better? AI research is broad, and the flexibility of computer programming is essentially limitless, so it’s hard to imagine what software development will look like when intelligent programs can help us interact with code.

But what many developers and tech managers don’t realise is that AI’s usefulness for development teams has made huge leaps in just the last several years. In fact, the early stages of AI-assisted software development are already here.

Automation Is Incomplete Without AI Assistance

It’s hard to find an organisation that doesn’t have efficient, agile software development as a goal, and automation technology has made agility achievable at scale. Automatically running tests has improved software quality in the last decade by allowing developers to get immediate feedback on their code changes and adjust accordingly. Automated software pipelines make use of robot assistants that generate pull requests, enabling the continuous delivery of updates. 

But the companies that have embraced the technology are sometimes finding that automation alone isn’t enough. Automated processes still have bottlenecks, most of which surround the creation of new code. For example, automating the execution of hundreds or thousands of unit tests can be done quickly, but it takes hours or weeks for the development team to write the tests themselves. Without tests to validate commits, automated pipelines promote junk. What would otherwise be an automatic process is broken up by the need for ongoing manual effort as new code (and new tests) are added. 

AI for Code

Currently, existing AI for code technology can address both of these issues in one go, by automatically writing test code that validates the rest of the automation pipeline. This type of task previously required a developer’s time, preventing them from doing the more fulfilling and value-adding work, such as creating new features. AI used for unit testing opens the door to the more complete automation of important, but slow and tedious, processes.

As might be expected, tests created by AI look different from those written by humans, but they will be produced in a fraction of the time and they function just as well, if not better, to ensure that any code-breaking changes are caught while it’s still easy to fix them. After all, as Martin Fowler has summed up in his 2006 article on Continuous Integration: “Imperfect tests, run frequently, are much better than perfect tests that are never written at all.”

In this way, AI can start to break down the trade-off between time, cost and quality of work that developers and IT managers struggle with. AI-assisted development can empower developers to create new products faster and more cost-effectively without compromising on quality. With repetitive tasks completed reliably and at speed, developers can get back to the creative tasks that attracted them to their jobs in the first place.

The Incredible Efficiency of AI 

In some industries that place a high value on the quality of their code, such as finance, AI-assisted software development is already in use. Goldman Sachs, for example, recently used AI for code to improve the efficiency of their software development. By leveraging an AI tool to write over three thousand unit tests for one legacy application with fifteen-thousand lines of code, they created an entire test suite in hours. Compared to spending an average of 30 minutes writing each unit test manually, the AI tool was able to write tests more than 180 times faster. All told, the bank managed to save over a year of developer time.

What’s Next?

As AI technology continues to advance and solutions for more use cases are developed, investments in AI for software development will become more common across industries. It won’t be long before integrating a new level of efficiency-boosting tools into the development process is a requirement in order to stay competitive and grow at scale. But in the meantime, the first iterations of AI-assisted software development are already here and giving a glimpse of what we can expect from the future of coding.

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The career boasts a current average salary of $146,085 with a growth rate of 344 percent last year.

Tech-related jobs, in general, continue to be winners. Indeed set out to find the top 25 jobs for 2019 in their report and nine of them are comprised of tech roles.

Roles such as software developer continue to rank highly due to a high number of job openings, but machine learning engineer roles claim the number one spot due to higher salaries and faster growth.

A second AI-related job sits just outside the top 10. At number 13, ‘Computer Vision Engineer’ has a higher average base salary ($158,303)  than a machine learning engineer, but is ranked lower due to slower growth (116%).

Here’s the full list of top jobs in Indeed’s report:

Due to the increasing use of AI in companies’ operations, the report expects this growth to continue accelerating in the coming years.

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