According to Google, this update has increased the accuracy of calculation-based word and math problems in their internal challenge datasets by 30%.

The tech giant claims that Bard should be able to provide information such as simple odds of numbers in the millions, the rate of increase in savings, and what words like “lollipop” look like in reverse (this is the strongest weapon the tool has against the competition).

Google also announced a feature that allows the data Bard produces to be edited and manipulated in Sheets. This is the perfect update for people who hate spreadsheets.

By 2023, software developer productivity could increase tenfold thanks to AI-powered programming assistants like Copilot, based on a 70% annual drop in marketing and feedback costs.

The programming process can be revolutionized by generative artificial intelligence, which will also significantly increase productivity. Generative AI can learn from huge datasets of code and create new, semantically and syntactically sound code using deep learning methods.

In particular, for routine tasks that require repetitive programming patterns, this can significantly reduce the time and effort required to write new code. Developers can focus on more complex and creative work by automating certain activities, which will increase overall productivity.

What is Generative Artificial Intelligence
Generative artificial intelligence uses deep learning algorithms to automatically create new content such as text, music and graphics. Software engineers can automate monotonous operations and free up their time for more creative work by using generative AI to create new code based on input or patterns from current code.

The AI model
The AI model can recommend existing code snippets that perform similar tasks, for example if the developer is working on a new feature that involves string manipulation. The programmer can then modify the proposed code to meet the requirements of the specific application, thus saving time and effort.

Generative AI saves time
Developers can automate time-consuming procedures using generative artificial intelligence. For example, several tools make code completion suggestions using machine learning, which cuts down on the time and effort required to develop code. These tools analyze the code, spot trends, and suggest code snippets that are appropriate for the code being written using a deep learning algorithm.

Developers have the opportunity for more creative and sophisticated work
Generative AI can also be very useful in detecting and correcting syntactic problems.
The productivity of software engineers is increasing dramatically as a result of the revolution that generative AI is causing in the coding process.

It allows developers to focus on more inventive and complex work by automating tedious processes, improving code quality and stability, and offering coding aids like Copilot.

MLX provides a collection of free, open-source, state-of-the-art deep learning models for common application domains. The curated list includes deployable models that can be run as a microservice on Kubernetes or OpenShift and trainable models where users can provide their own data to train the models.

It provides developers and data scientists with automated sample pipeline code generation to execute registered models, datasets, and notebooks, and a pipelines engine powered by Kubeflow Pipelines on Tekton, the core of Watson Studio Pipelines.

It also provides a registry for Kubeflow Pipeline Components, dataset management by Datashim, and a serving engine by KFServing. The contributors to the project said:

“Due to the large number of steps that need to be worked on in the Data and AI lifecycle, the process of building a model can be bifurcated amongst various teams and large amounts of duplication can arise when creating similar Datasets, Features, Models, Pipelines, Pipeline tasks, etc. This also poses a strong challenge for traceability, governance, risk management, lineage tracking, and metadata collection.

To solve the problems mentioned above, contributors need a central repository where all the different asset types like Datasets, Models, and Pipelines are stored to be shared and reused across organizational boundaries.

In last year’s survey, the number of developers claiming this responsibility was less than 50%, supporting the notion that the role of software developers has expanded beyond writing code. As organizations move security, testing, governance and even marketing and finance earlier into the application life cycle, developers are squeezed for the time by ever-shrinking delivery timelines, and data quality often remains a “hope it’s right” afterthought to development teams.

Among the other key findings is that the top problem development teams face is the inconsistency of the data they need to utilize, followed closely by incomplete data and old/incorrect data. Last year’s top choice, duplicate data, fell to fourth this year. Misfielded data and international character set to round out the category.

Because of these data problems, respondents to the survey said they spend about 10 hours per week dealing with data quality issues, taking time from building new applications. Despite these problems, some 83% of respondents claimed their organizations are either data proficient or data-aware, while only the remainder say they are data-savvy (15%) and data-driven (around 2%). According to the publisher:

“Data is critical to the success of organizations worldwide, and to find that such a small number consider themselves savvy or data-driven is somewhat alarming. With the world moving forward on data privacy and governance, to see organizations still failing to maintain their data should be a wake-up call for the industry at large.”

James Royster, the head of analytics at Adamas Pharmaceutical and formerly the senior director of analytics and data strategy for biopharmaceutical company Celgene said the big problem organizations face with their data is that there are “thousands of nuances” in big sets of data.

Royster gave an example of IQVIA, a health care data connectivity solutions provider, which collects data from more than 60,000 pharmacies, each dispensing hundreds and thousands of drugs, serums and more. On top of that, they service hospitals and doctors’ offices.  So, he explained, “there are millions of potential points of error.”  And in order for companies to create these datasets, they have to have developers write code that brings these data sets together, in a way that can be digested by a company. And that’s an ongoing process. “So as they’re changing code, updating code, collecting data, whatever it is, there are millions of opportunities for things to go wrong.”

But data issues don’t occur only in large organizations. Smaller companies also have problems with data, as they don’t have the resources to properly collect the data they need and monitor it for changes beyond someone in the database contacting them that something in their data has changed.

As an example, smaller companies might use a form to collect data for users, but many users provide bad data to avoid unwanted contact. The problem, Royster said, is that there’s nobody checking it or aggregating it or applying any sort of logic to it to say, this is how this should be. He concluded that it’s just data goes in, data comes out. And if that data that goes in is incorrect, what comes out is incorrect too.

The companies have been working together since November 2019 with a commitment to ‘redefining the Department of Defense’s logistics strategy with the use of additive manufacturing. This involves analysing the feasibility of additive manufacturing to increase ‘combat readiness and warfighter innovation.’

With the use of 3YOURMIND’s software, the US government is able to identify AM-ready spare parts and print them on-demand, utilising a digital warehouse of part designs. 3YOURMIND’s software platform analyses CAD data and develops a library of files categorised by technical and economic metrics, which are used to determine if a part is suitable for additive technology and what time or cost advantages can be attained.

As well as its technology, 3YOURMIND also supports and trains Phillips Corporation through a consultative approach called the P.R.E.P process. P.R.E.P consists of preparing initial datasets with default, market-validated algorithms; refining algorithms, profiles and workflows per usage scenario; evaluating output, adjusting metadata analysis, cleaning data and expanding to additional datasets; and presenting accurate algorithms and AM use cases.

By working with 3YOURMIND, Phillips Corporation is aiming to modernise the national defence system, increase material readiness and enhance warfighter capability. Tim McClanahan, Business Development Manager at Phillips Corporation commented:

“We are thrilled to announce this partnership with 3YOURMIND. Our AM Innovation Center includes the most advanced hardware, software and services on the market. Adding 3YOURMIND software enables us to provide many more proactive solutions to our military partners for leveraging the full potential of their additive manufacturing assets.”

According to William Cuervo, Senior Business Development Manager USA at 3YOURMIND, 3YOURMIND and Phillips Corporation share a similar vision to reimagine the supply chain. He reassured that the Agile PLM software, equipped with a sophisticated part identifier tool, is a secure programme that provides data transparency and streamlined communications that enable defence organisations to take decisive actions.

The idea is that its dashboards and alerts provide a constant readout of how engineering projects are developing, enabling managers to identify potential sources of delay, eliminate bottlenecks and drive productivity. The result, Haystack argues, is good for the business and good for developers too in an industry where burn-out and employee well being have become increasingly pressing issues in recent times. Colina commented:

“Kan and I launched Haystack to solve exactly the kind of problems we had seen during our own careers leading development projects. Something like $400bn a year is now being spent on engineering salaries around the world but there is almost no data on what people are doing each day; there is a complete lack of visibility on how teams are performing and where they may need more help.”

Julian Colina; Photo Credits: Haystack

Haystack seeks to remedy that problem through a platform that integrates with GitHub, through which so many developers collaborate and manage projects. That enables it to prove live data insights as a project moves forward. Managers get alerts when problems in a particular area are building up, enabling them to intervene before the issue causes knock-on effects and delays elsewhere.

The results are dramatic. Users have, on average, increased their production deployments by 58% and achieved 70% faster cycle times. That has persuaded more businesses to join up. Just 12 months after its launch, Haystack is now being used by more than 7,000 developers, with technology teams at businesses including Microsoft, GoDaddy, The Economist and Indiegogo all making use of its platform. Colina also adds:

“Companies with strong technology teams are outperforming those which don’t have such strength. Technology teams that follow best practice are less likely to see developers being burned out while enabling faster experimentation, and shipping business value more reliably.”

The first of those points is particularly crucial in an environment where employee wellbeing issues and mental health have moved the front of mind for responsible businesses. He also noted:

“The burn-out issue in the technology sector has been completely unaddressed. Engineers are expected to work on release after release and come under huge pressure.”

Haystack’s argument is that by bringing structure and efficiency to the development process, that pressure can be mitigated and managed. And the bottom-line gains for businesses are enormous: research suggests that elite performing engineering organizations are twice as likely to achieve their organizational goals and that they achieve a 50% higher growth rate over three years.

The company’s backers recognize the potential of a business that is growing fast in its own right, with Haystack having seen its revenues increase at an average of 36% a month over its first year in business. As well as existing backer Y Combinator, Haystack’s new funding includes commitments from investors including Founders Club, Soma Capital, Entrepreneur First and investors Dan Siroker and Blaine Vess.

The extra capital will help Haystack build more comprehensive datasets, improving the quality of the insight it supplies to users through its algorithms and analytics. “We pride ourselves on supporting development speed,” Colina says. “The more data we have, the more powerful the opportunity will be for our users to capitalize on our insight.”