Organizations should consider adopting AutoML to ease the process of data analytics by automating the process.

Industries have been leveraging AutoML to enhance data processing and data engineering. However, there are discussions of how AutoML will affect the job of data scientists. Let us understand more about this technology and its role in enhancing the data efficiency of a company.

The digitization and automation across organizations demanded the adoption of data science and advanced data analytics to encourage business growth and agility. With this increased pace of transformation, companies started to employ data scientist teams to address the need for developing machine learning models and analytics algorithms.

Data-driven decision-making in organizations has proved to improve productivity and minimize costs in the long run. Due to the highly technical skills required for the job, the supply of data scientists is limited even now, thus making it difficult for organizations to capitalize on data and create machine learning models to analyze them. This is where AutoML comes in.

Why AutoML?

Automated Machine Learning is a nascent development in the field of artificial intelligence. AutoML automates the end-to-end machine learning requirements in business operations. This technology enables the development and deployment of machine learning models without any time or skill constraints.

The conventional procedure by data scientists takes a good portion of time since it involves data cleaning, data analysis, identifying machine learning models, running them, conducting parameter tuning, designing the algorithms, and deploying them. Integrating this long process into the workflow of organizations can be difficult and time-consuming. Since there is a shorter supply and high demand for data scientists, it becomes tougher to develop a team.

Automated Machine Learning eliminates all these challenges by automating the process and running several machine learning models at the same time. AutoML also aids the process of feature selection, feature extraction, and feature engineering to run algorithms. The amount of data is increasing each day and so is the adoption of big data in organizations. Hence, AutoML is a desirable technology to reduce the time and complexity in the implementation of machine learning models.

Another commendable benefit of employing AutoML is its role in the democratization of data science in organizations. There is a huge skill gap in most companies concerning the high skill demand for data science. Organizations usually find it difficult to address the need for better machine learning models because of the limited access of people to the field of data science. AutoML for organizations eliminates this gap by encouraging ‘citizen data scientists ’ to perform the tasks without any prior expertise.

It enables employees other than people with data scientist qualifications to contribute to the data science ecosystem with minimal assistance from the data science teams. For example, Cloud AutoML by Google enables businesses to build customized machine learning models with limited skills and expertise in the field. AutoML increases the accessibility of data science and data engineering to a larger audience rather than restricting it to a popular group.

Will AutoML Eliminate Data Scientists?

If you want a single-word answer then, No-AutoML will not make data scientists disappear. It will ease the burden on the shoulders of these data experts by taking over repetitive tasks that do not need much attention. AutoML will automate some of their tasks and leave them with those that need highly technical skills. Organizations will still need data scientists to define problems, apply domain knowledge on the issue, and generate reasonable and creative models. AutoML can work alongside data scientists to support them and this course will enable the decentralization of data science knowledge.

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AutoML (automated machine learning) is an active area of research in academia and the industry. The cloud vendors promote some or the other form of AutoML services. Likewise, Tech unicorns also offer various AutoML services for its platform users. Additionally, many different open source projects are available, offering exciting new approaches.

The growing desire to gain business value from artificial intelligence (AI) has created a gap between the demand for data science expertise and the supply of data scientist. Running AI and AutoML on the latest Intel architecture addresses this challenge by automating many tasks required to develop AI and machine learning applications.

How it Functions

Using AutoML, businesses can automate tedious and time-consuming manual work required by today’s data science. With AutoML, data-savvy users of all levels have access to powerful machine learning algorithms to avoid human error.

With better access to the power of ML, businesses can generate advanced machine learning models without the requirement to understand complex algorithms. Data scientists can apply their specialisation to fine-tune ML models for purposes ranging from manufacturing to retailing to healthcare, and more.

With AutoML, the productivity of repetitive tasks can be increased as it enables a data scientist to focus more on the problem rather than the models. Automating ML pipeline also helps to avoid errors that might creep in manually. AutoML is a step towards democratizing ML by making the power of ML accessible to everybody.

Automating ML Workflow

Enterprises seek to automate machine learning pipelines and different steps in the ML workflow to address the increase in tendency and requirement for speeding up AI adoption.

Not everything but many things can be automated in the data science workflow. The pre-implemented model types and structures can be presented or learnt from the input datasets for selection. AutoML simplifies the development of projects, proof of value initiatives, and help business users to stimulate ML solutions development without extensive programming knowledge. It can serve as a complementary tool for data scientists that help them to either quickly find out what algorithms they could try or see if they have skipped some algorithms, and that could have been a valuable selection to obtain better outcomes.

Here are some reasons why business leaders should hire data scientists if they have AutoML tools on their hands:

• Data science is like any other business function that must be performed with due diligence and needs creative thinking and human skills to get the best results.
• Data science is like babysitting, and one has to take care of the ML models, data and other assets regularly.
• AutoML is still in infancy. Once it’s ready, living without data scientists could be possible, at least for the most part.
• When one gets the results and business insights, the individual would still need the data workers to interpret them and communicate them to business.

Future of AutoML

Essentially, the purpose of AutoML is to automate the repetitive tasks like pipeline creation and hyperparameter tuning so data scientists can spend time on the business problem at hand.

AutoML aims to make the technology available to everyone rather a select few. AutoML and data scientists can work in conjunction to speed up the machine learning process to utilise the real effectiveness of ML.

Whether or not AutoML becomes a success depends mainly on its adoption and the advancements that are made in this sector. However, AutoML is a big part of the future of machine learning.

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Machine learning depends on data scientists to handle the ML configurations and data inputs

Machine Learning (ML) is constantly being adopted by diverse organizations in an enthusiasm to acquire answers and analysis. As the embracing highly increases, it is often forgotten that machine learning has its flaws that need to be addressed for acquiring a perfect solution.

Applications of artificial intelligence and machine learning are using new tools to find practical answers to difficult problems. Companies move forward with the emerging technologies to get a competitive edge on their working style and system. Through the process, organizations are learning a very important lesson that one strategy doesn’t fit for all. Business organizations want machine learning to do analysis on large data, which is complex and difficult. They neglect the fact that machine learning can’t perform on diverse data storage and even if it does, it will conclude with a wrong prediction.

Analysing unstructured and overwhelming large datasets on machine learning is dangerous. Machine learning might conclude with a wrong solution while performing predictive analysis on such data. The implementation of the misconception in a company’s working system might drag down its improvement. Many products that incorporate machine learning capabilities use predetermined algorithms and many diverse ways to handle data. However, each organization’s data has different technical characteristics that might not go well with the existing machine learning configuration.

To address the problems where machine learning falls short, AutoML takes head-on in the company’s data analysis perspective. AutoML takes over labour intensive job of choosing and tuning machine learning models. The new technology takes on many repetitive tasks where skilful problem definition and data preparation are needed. It reduces the need to understand algorithm parameters and shortening the compute time needed to produce better models.

Machine Learning is not Sorcery

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The technology focuses on the development of computer programs that can access data and use it for themselves. It is a model created and trained on a set of previously gathered data, often known as outcomes. The model can be used to make predictions using that data.

However, machine learning can’t get accurate results all the time. It depends on the data scientist handling the machine learning configurations and data inputs. A data scientist studies the input data and understands the desired output to solve business problems. They choose the apt mathematical algorithm from a dozen and tune those parameters called ‘hyperparameters’ and evaluate the resulting models. The data scientist has the responsibility to adjust the algorithm’s tuning parameters again and again until the machine learning model produces the desired result. If the results are not tactic, then the data scientist might even start from the very beginning.

Machine learning system struggles to function when the data is too large or unorganised. Some of the other machine learning issues are,

• Classification- The process of labeling data can be thought to as a discrimination problem, modeling the similarities between groups.

• Regression- Machine learning staggers to predict the value of a new unpredicted data.

• Clustering- Data can be divided into groups based on similarity and other measures of natural structure in data. But, human hands are needed to assign names to the groups.

Machine learning problems

As mentioned earlier, machine learning alone can’t address the datasets of an organisation to find predictions. Here are some reasons why tuning a machine learning algorithm is challenging to choose and how AutoML can prove to be useful at such instances.

Choosing the right algorithm: It is not always obvious to choose a perfect algorithm that might work well for building real-value predictions, anomaly detection and classification models for a particular data set. Data scientists have to go through many well-known algorithms of machine learning that could suit the real-world situation. It could take weeks or even months to come up with the right algorithm.

Selecting relevant information: Data storage has diverse data variables or predictors. Henceforth, it is hard to tell which of those data points are significant for making a decision. This process of selecting relevant information to include in data models is called ‘feature selection.’

Training machine learning models: The most difficult process in machine learning is to choose a subset of data that can be used for training a machine learning model. In some cases, training against some data variables or predictors can increase training time while actually reducing the accuracy of the ML model.

AutoML helps machine learning out of the chaos

Automated machine learning (AutoML) basically involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry. AutoML makes well-educated guesses to select a suitable ML algorithm and effective initial hyperparameters. The technology tests the accuracy of training the chosen algorithms with those parameters and makes tiny adjustments, and tests the results again. AutoML also automates the creation of small, accurate subsets of data to use for those iterative refinements, yielding excellent results in a fraction of the time.

In a nutshell, AutoML acts as a right tool that quickly chooses, builds and deploys machine learning models that deliver accurate results.

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KNIME and H2O.ai, the two data science pioneers known for their open source platforms, announced a strategic partnership that integrates offerings from both companies. The joint offering combines Driverless AI for AutoML and KNIME Server for workflow management across the entire data science life cycle – from data access to optimization and deployment. With this partnership, KNIME and H2O.ai offer a complete no-code, enterprise data science solution to add value in any industry for end-to-end data science automation.

Preparing data for AI, selecting the right model, pushing it into production, and continuously optimizing it is a process that typically requires many stakeholders and several tools. Parts of it can be automated, but flexibility is paramount to select the techniques that answer a company’s questions in the best way. The lack of an end-to-end tooling prevalent in most data practices also makes it very difficult to ensure data lineage. This H2O.ai and KNIME integration now provides a solution that covers all these challenges as well as increases data scientists’ productivity, reduces overall IT spend, and creates and uses more accurate predictions.

The expanded integration between H2O.ai and KNIME brings together all-encompassing, intuitive, automated machine learning from H2O.ai with the guided analytics from KNIME.

Customers of H2O.ai and KNIME can now:

• Develop an integrated data science workflow in KNIME Analytics Platform and KNIME Server, from data discovery and data preparation to production-ready predictive models.
• Deliver the power of automatic machine learning to business analysts, enabling more citizen data scientists with H2O Driverless AI.
• Reduce model deployment times, leveraging H2O Driverless AI and KNIME Server for reliably managing the workflow and creation process in production.

“We have been using KNIME and H2O Driverless AI for years, and we are very excited about this new integration and the automation and simplification that it will bring to our data science workflow,” said Alejandro Lopez, data science leader of Vision Banco.

“H2O Driverless AI users can now get an integrated data access and preparation platform with KNIME. This allows seamless operationalization and continuous learning demanded by our customers adapting at the speed of change today,” said Sri Ambati, CEO and founder of H2O.ai.

“The integration of Driverless AI offers KNIME users a strong, additional option to automate machine learning out of the box with a huge range of powerful algorithms. We believe that flexibility of choice brings most value to our users and customers, and H2O is a great addition to the mix,” said Michael Berthold, CEO and co-founder of KNIME.

H2O is a leading open source AI platform, and its Driverless AI is a leading automatic machine learning (AutoML) platform. H2O Driverless AI automates time-consuming machine learning workflows with automatic feature engineering, model tuning, and model selection to achieve the highest predictive accuracy within the shortest amount of time. H2O Driverless AI empowers data scientists, statisticians and domain scientists to work on projects faster and more efficiently by using automation to complete tasks that can take months in minutes or hours. It can now be used within a KNIME workflow.

KNIME Analytics Platform and KNIME Server provide a visual workflow platform for ETL, further machine learning choices, deployment, collaboration, and cloud execution. Users can blend and transform data from hundreds of data sources using a visual, no-code, fully auditable approach. KNIME also offers a wide range of options for how the output can be deployed — from REST to web applications, BI dashboards, and other third-party tools. With Integrated Deployment, teams can automatically and continuously deploy and update models including the process of data access and preprocessing. Driverless AI adds a powerful choice for automating machine learning.

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Hyperparameters are usually tuned by a human operator such as an ML engineer. This is still a standard practice despite the great success of AutoML platforms. Though there is no doubt that businesses are more readily embracing AutoML tools, the role of a human operator cannot be disregarded. So, now the question is — does the result of machine learning models depend on the competencies of the human operator. The answer is, of course, a plain YES. But that wouldn’t suffice. Organisations invest heavily in picking the right candidate. So, it is crucial to know about this aspect in more detail.

To find out, researchers from Delft University of Technology, Delft, The Netherlands surveyed a group of ML engineers of varying expertise. The results of this survey were published recently in a paper titled, ‘Black Magic in Deep Learning: How Human Skill Impacts Network Training’.

The extraordinary skill of a human expert to tune hyperparameters, wrote the researchers, is informally referred to as “black magic” in deep learning here.

For the experiment, the researchers selected the Squeezenet model as they found it to be efficient to train and achieve a reasonable accuracy compared to more complex networks. To prevent exploiting model-specific knowledge, they did not share the network design with the participants.

Participants were given access to 15 common hyperparameters. Mandatory ones were — number of epochs, batch size, loss function, and optimiser. The other 11 optional hyperparameters were set to their default values.

Taking size and difficulty into account, the participants were given an image classification task on a subset of ImageNet. The name was kept under wraps, and only the image classification task was revealed to them along with the dataset statistics that consists of 10 classes, 13,000 training images, 500 validation images, and 500 test images.

The whole experimental procedure can be summarised as follows:

The participants enter their information.
Hyperparameter values and evaluates intermediate training results are submitted.
Once training is finished, the participant can either submit a new hyperparameter configuration or end the experiment.
This is repeated until the clock ticks 120 minutes.
The researchers segregated the participants based on the number of months of the deep learning experience. They collected a total of 463 different hyperparameter combinations from 31 participants. Of which, the Novice group contained 8 participants with no experience in deep learning, the 12 participants with less than nine months of experience and the rest with more than nine months experience.

Whenever a participant submitted their final choice of hyperparameters, the experiment ended, and the optimal hyperparameter configuration was then trained 10 times. “Each of the 10 repeats has a different random seed, while the seeds are the same for each participant,” stated the researchers.

The results showed that human skills do impact accuracy. Few other key findings from this survey are:

Even for people with similar levels of experience in tuning the model performed differently.
Even for experts, there can be an accuracy difference of 5%.
More experience correlates with optimisation skill.
The trend shows a strong positive correlation between experience and the final performance of the model.
Inexperienced participants usually followed a random search strategy, where they often start by tuning optional hyperparameters which may be best left at their defaults initially.
On a concluding note, the team behind this work shared a couple of insightful recommendations. The authors underlined the importance of reproducibility and urged to share the final hyperparameter settings. And, since it is difficult to say if the purported superior performance is due to a massive supercomputer, they advise reviewers to pay more attention to reproducibility, baseline comparisons and put less emphasis on superior performance.

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Many of us are familiar with the concept of machine learning as it pertains to neural networks. But what about TinyML?

Surging Interest in TinyML

TinyML refers to the machine learning technologies on the tiniest of microprocessors using the least amount of power (usually in mW range and lower) while aiming for maximized results.

With the proliferation of IoT devices, big names like Renesas and Arm have taken a vested interest in TinyML—for instance, with Arm’s recent expansion of its AI portfolio with new machine learning and neural processing IP and Renesas’ release of its TinyML platform, Qeexo AutoML, which does not require code nor expertise in ML. 

Other companies have zeroed in on partnerships that will help them exaggerate the utility of TinyML. Eta Compute and Edge Impulse recently announced their partnership in which they’ll combine the strengths of Eta Compute’s neural sensor processor, the ECM3532, with Edge Impulse’s tinyML platform. With an eye on battery capacity—a difficult point to work around in TinyML—this partnership hopes to accelerate the time-to-market of machine learning in billions of low-powered IoT products. 

Another way we can assess the progress of TinyML is to reflect on the tinyML Summit, which took place earlier this year. Several of the presentations at the conference illustrate the key concepts of machine learning at the smallest level. 

Reflections on the tinyML Summit

In February, AAC contributor Luke James forecasted the high aims for the 2020 tinyML Summit, which would, as in years past, spotlight developments in TinyML. The summit published presentations online and explored a number of categories pertaining to TinyML: hardware (dedicated integrated circuits), systems, algorithms and software, and applications.

Here are a few noteworthy presentations as they relate to design engineers.

Model Compression

Two of the presenters at the conference brought the realities of tinyML into focus by discussing a device we all have: mobile phones. In their discussion of “model compression,” MIT researcher Yujun Lin explained that typical machine learning devices, such as cell phones, have approximately 8 GB of RAM while microcontrollers have approximately 100 KB to 1 MB of RAM. Because microcontrollers have weight and activation constraints, they necessitate model compression.

The concept is to shrink the pre-trained large models into smaller ones without losing accuracy. This can be achieved in processes like pruning and deep compression. Pruning parses out synapses and neurons, resulting in ten times fewer connections. Deep compression takes pruning a step further with quantization (fewer bits per weight) and a technique known as “Huffman Encoding.” 

The researchers suggested that by combining a concept known as neural-hardware architecture search with non-expert usage into the neural network, we can improve AI-geared hardware. The VP and lab director of Samsung’s Advanced Institute of Technology, Changkyu Choi, went into further detail on deep model compression, but his focus was on acceleration toward on-sensor AI. 

Deep Reinforcement Learning

Another expert, Hoi-Jun Yoo, the ICT endowed chair professor from the engineering school at KAIST (Korea Advanced Institute of Science and Technology) spoke about the importance of deep reinforcement learning (DRL) accelerators within the deep neural network (DNN).

In his discussion, he points out that “software and hardware co-optimization for DNN training is necessary for low-power and high-speed accelerators in the same way it brought a dramatic increase in the performance of DNN inference accelerators.”

Yoo also explains that DRL is an essential factor in TinyML because it enables continuous decision-making in a low-power, “unknown environment,” or an environment in which labeled data is difficult to capture. 

DNNs for Always-on AI for Battery-Powered Devices

Another company, Syntiant, showcased one of their devices, the NDP100 neural decision processor (NDP), to discuss a broader concept: the value of deep learning over algorithmic genius. Dr. Stephen Bailey, CTO of Syntiant, explained that the magic of the company’s NDP, an always-on and “listening” device, is its deep neural networks (DNN)—continuing Yoo’s discussion on DNNs. 

The Syntiant NDP feeds acoustic features to a large DNN (no need for cascading or energy gating) and trains the DNN with large data sets and wide-ranging augmentation. Beyond its noise immunity, the NDP100 is extremely small in size (1.4 mm x 1.8 mm) and consumes less than 140 μW. 

Since the summit, Syntiant has also released the NDP101, which is said to couple computation power and memory to exploit “the vast inherent parallelism of deep learning and computing at only required numerical precision.” Syntiant says that these features improve efficiency by 100 times compared to the stored program architectures you’d see in CPUs and DSPs. 

Smaller Devices Call for Compressed Machine Learning

The hardware requirements for machine learning in larger systems are similar for TinyML in small IoT. But sometimes, the stakes are higher because of the device’s small size: accuracy, latency, and power consumption. As smaller IoT devices hit the market, engineers may increasingly dabble in TinyML, familiarizing themselves with concepts like deep neural networks, model compression, and deep reinforcement learning. 

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Data Science and Machine Learning are among the most deployed and useful technologies of the current marketplace. And as the utility increases, the new wave of advancements hit the industry with more innovations in its tides. Similarly, to add an extra edge to what Data Science and ML could achieve, we now have AutoML (Automated Machine Learning) platforms. It is among the top trends of contemporary data-market with most of the big techs investing in its successful incorporation. Companies including Google, Amazon, Microsoft have already embraced AutoML in their business processes to accelerate the effectiveness of their operations and products. Considered as a quiet revolution in AI, the technology has transformed the entire data science landscape while offering a great deal to modern-day businesses.

Let’s know what AutoML actually is?

Automated machine learning (AutoML) is the process to automate an end-to-end process of leveraging machine learning algorithms to real-world problems. One of the most peculiar features of the technology is that even people with no data science or ML expertise can work with this platform to carry out desired outcomes.

But why do we need AutoML?

According to Gartner’s survey, it takes around 4 years to make an AI project go live which doesn’t cope-up with the rising demand and transforming market dynamics. And, according to statistics, huge investments in data and AI projects are only successful 15% of the time. However, with the rise in current trends and the AutoML platform, small AI projects can be produced in a short period of time.

Moreover, the soaring demands for machine learning systems don’t imply the successful deployment of ML models across a wide range of applications. Its success requires a proficient team of seasoned data scientists and a team that decides which model is the best for a particular business problem. But the shortage of data science talents has doesn’t quite fulfilled the scenario. Here enters the AutoML platform which tends to automate the maximum number of steps in an ML pipeline while reducing the human effort without compromising on the quality of performance.

So how is it changing the landscape of modern businesses?

Have you heard of Mercari? Mercari is a popular online shopping app in Japan. The company uses Google’s AutoML tool in order to better process the image classification. Using a UI for uploading photos, Mercari’s app can identify and suggest brand names from over 12 major brands through customized AutoML pipeline technology.

Leveraging Google’s AutoML platform enabled the company to customize ML models in successfully identifying over 50,000 images with an accuracy of 91.3%.

Moreover, the implementation of automated machine learning across physical retail stores is redefining their future with rich business benefits including better sales forecasting and significant others. Analyzing the available current customer data and purchasing season, the AutoML platform can help retail industry businesses with better sales prospects. This can subsequently reduce the unused inventory costs and waste in unnecessary promotions.

While leveraging the AutoML to enhance business effectiveness and productivity, brands can also improve customer personalization through customization.

For any business across any industry, AutoML is bound to make cost reductions and increase productivity for data scientists while the democratization of machine learning reduces demand for them. The technology also helps accelerate revenues and customer satisfaction. AutoML models with enhanced accuracy possess the capability to improve other, less tangible business results too.

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Machine Learning has been serving several industries for the past many years. It has enabled businesses to work easily with data. Moreover, the acceleration in the adoption of ML tools has evolved with time making it even easier to use today. Using AutoML tools, the act of gathering data and turning it into actionable insights has become much convenient. People with even less knowledge of data science and machine learning can work with these automated tools.

DataRobot

In 2013, DataRobot invented automated machine learning — and an entirely new category of software as a result. Unlike other tools that provide limited automation for the complex journey from raw data to return on investment, the company’s Automated Machine Learning product supports all of the steps needed to prepare, build, deploy, monitor, and maintain powerful AI applications at enterprise scale.

DataRobot’s AutoML product accelerates the productivity of your data science team while increasing your capacity for AI by empowering existing analysts to become citizen data scientists. This enables your organization to open the floodgates to innovation and start your intelligence revolution today.

Google Cloud AutoML

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.

Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions.

dotData

dotData was born out of the radical idea, unique among machine learning companies, that the data science process could be made simple enough for just about anyone to benefit from it. Led by Dr. Ryohei Fujimaki, a world-renowned data scientist, and the youngest research fellow ever appointed in the 119-year history of NEC, dotData was created to accomplish this mission. The company values its clients and works hard to provide the highest value possible in Automated Machine Learning (AutoML).

dotData was first among machine learning companies to deliver full-cycle data science automation for the enterprise. Its data science automation platform speeds time to value by accelerating, democratizing, and operationalizing the entire data science process through automation.

Splunk

Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies, and generating predictions for future values.

H2O

H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

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It looks like Google‘s working on some major upgrades to its autonomous machine learning development language ‘AutoML.’ According to a pre-print research paper authored by several of the big G’s AI researchers, ‘AutoML Zero’ is coming, and it’s bringing evolutionary algorithms with it.

AutoML is a tool from Google that automates the process of developing machine learning algorithms for various tasks. It’s user-friendly, fairly simple to use, and completely open-source. Best of all, Google‘s always updating it.

In its current iteration, AutoML has a few drawbacks. You still have to manually create and tune several algorithms to act as building blocks for the machine to get started. This allows it to take your work and experiment with new parameters in an effort to optimize what you’ve done. Novices can get around this problem by using pre-made algorithm packages, but Google‘s working to automate this part too.

Per the Google team’s pre-print paper:

It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space.

Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging.

Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available.

In other words: Google‘s figured out how to tap evolutionary algorithms for AutoML using nothing but basic math concepts. The developers created a learning paradigm in which the machine will spit out 100 randomly generated algorithms and then work to see which ones perform the best.

After several generations, the algorithms become better and better until the machine finds one that performs well enough to evolve. In order to generate novel algorithms that can solve new problems, the ones that survive the evolutionary process are tested against various standard AI problems, such as computer vision.

Perhaps the most interesting byproduct of Google‘s quest to completely automate the act of generating algorithms and neural networks is the removal of human bias from our AI systems. Without us there to determine what the best starting point for development is, the machines are free to find things we’d never think of.

According to the researchers, AutoML Zero already outperforms its predecessor and similar state-of-the-art machine learning-generation tools. Future research will involve setting a more narrow scope for the AI and seeing how well it performs in more specific situations using a hybrid approach that creates algorithms with a combination of ‘Zero’s’ self-discovery techniques and human-curated starter libraries.

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Science-fiction author Vernor Vinge once said that mankind’s last great invention will be the first self-replicating machine. Now, AI scientists working in Google Brain division are testing how machine learning algorithms can be created from scratch, then evolve naturally, based on simple math, according to Google’s AutoML team who suggest the software could potentially be updated to “automatically discover” completely unknown algorithms while also reducing human bias during the data input process. The software, known as AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.

Machine learning tools are “trained” to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience.

But there’s a draw back, “Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML,” according to the team’s paper. “Innovation is also limited by having fewer options: you cannot discover what you cannot search for.” The analysis, which was published last month on arXiv, is titled “Evolving Machine Learning Algorithms From Scratch”.

In an interview with Newsweek, Haran Jackson, the chief technology officer (CTO) at Techspert, who has a PhD in Computing from the University of Cambridge, said that AutoML tools are typically used to “identify and extract” the most useful features from datasets—and this approach is a welcome development.

“There is a sense,” he added, “that among many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised. This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms.”

Jackson concluded that the approach taken was similar to the theory of evolution proposed by Charles Darwin, noting how the Google team was able to induce “mutations” into the set of algorithms. “The mutated algorithms that did a better job of solving real-world problems were kept alive, with the poorly-performing ones being discarded.

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