Business leaders see much in Artificial Intelligence, including new ways to save money, serve customers, and figure out what to build. What’s often tougher is deciding how to engage. That’s understandable, since getting into AI often raises questions about costs, data integrity, project length, and similar issues of planning and execution.

For CIOs concerned with the deployment and use of IT at their companies, these are critical issues. Here is one way to think about this complexity, by breaking it down into three different areas; Early Automation, Learning and working, and System Views.

First, a bit of good news and myth dispelling for readers who think AI is some far off promise, still in the labs and not safe for work. The reality is that AI is very much here, and almost everyone in your workplace is using it every day. People work with AI when they use Google Search, Photos, the autocomplete feature in Docs, live translation in Pixel phones, and many other areas. It is in the products of many other companies too.

“IDC reports that the market for AI software, hardware and services is expected to break the $500 billion mark in 2023.”

Additionally, it’s increasingly clear that AI can be adapted as a virtuous process, not just a one-off purchase (though there is much to recommend that.) During a recent Alphabet earnings call, CEO Sundar Pichai noted that “investments in AI will be key” to its near-term strategy, with new techniques that make it faster and easier to train and build AI for a number of uses. Additionally, the company is offering AI-driven “insights, new tools, and automation” to its advertising clients. The striking thing in this was the way that developing AI in one area could lead to growth in many others.

So, how does an IT leader foster a growth process like this for their stakeholders? By leading people through the well-established stages of Awareness, Learning, and Extension. Here’s what I mean.

Awareness: Early Automation

Consumer-facing AI is particularly strong in communications functions like voice recognition, translation, and writing tips. It’s similar in business uses: One of the most effective early instances of AI in the workplace has been Contact Center AI (CCAI), which manages basic customer communications, automatically answering common questions and prioritizing calls that require human assistance. It is doing what automation has always done best, automating the rote stuff and leaving the higher-value imaginative activity to people. It has been used by governments, retailers, telecommunications companies, and others, in a wide variety of use cases.

These and similar language-centric products, like DocAI for extracting information from things like invoices, receipts, or AI that extracts information from business contracts, have a number of benefits. For one, the investment is relatively easy to control, unlike with a research project leading to a formal launch. The payoff is also clearer. In the case of contact centers, in particular, the automation relieves stress, wins loyalty and slows disaffection in a high-turnover area. In both cases, successful results build allies in the business, who can testify to the earlier benefits when it’s time to take on something more complex.

Perhaps best of all, creating interest in basic AI services for business, right now, means people become engaged in learning more, since they see the early benefits and wonder what else might be done.

Learning: The Human Factor

The Natural Language Processing (NLP) that goes into these ready made, “out of the box” AI products can, not surprisingly, be used on much more sophisticated levels. Twitter, for example, processes 400 billion different events in real time, and its staff queries this trove using advanced NLP, answering questions and improving customer experiences.

There is clearly an enormous gap between Call Center AI and processing Twitter’s 400 billion events per day, but it’s not noticed enough how quickly that gap is closing. Look at how many products, partners, and training resources have emerged in the past few years. The gap makes sense, insofar as both the means of AI (like large data sets, good algorithms, and sufficient computing) and the value of AI, are new.

Increasingly, as AI is incorporated into standard enterprise tools like spreadsheets and analytic tools, easier to use AI becomes a skill within reach for many (even as the advanced end becomes more complex, meaning this ease of use process will continue for some time.)

It’s so new that AI skills demand isn’t met by conventional education means, creating lots of good opportunities for both nonstandard skills training, and in-house learning in the workforce. Companies offering AI skills training could well gain a competitive advantage and retain staff better.

Extension: Building System Views

When a new technology lands and gains in popularity, people seek to find new uses for it, or build connections among its different uses. Networked computing is one example, but think also of the way cars were soon followed by trucks and fire engines, or the way the data services on wireless phones soon morphed into the App Economy. If something is useful, people look for ways to grow it.

How will AI grow? My colleague Dominik Wee recently wrote about ways that AI will soon change supply chains, change product design, and improve sustainability. Most interestingly, he talked about how customers in manufacturing were realizing savings and gaining insights when once separate quality control data was combined with system wide views of the quality process.

There are several reasons to think AI will promote many such system views. For one thing, successful AI promotes the collection of data from more places, at greater frequency, since that leads to insight (and the cost of data collection is dropping.) Additionally, AI is good at spotting patterns and interactions that are not currently known. As well, AI is used in prediction and scenario planning, which leads to better understanding of how large-scale systems interact.

This comes at a time when we have more ways of seeing the world, from satellites, sensors, social media, and much more. We have more awareness of interactions, and a demand to understand them, in everything from the supply chain crisis, to human rights and sourcing regulations, or in the business realities of partnering, and serving customers in all sorts of ways, online and in the physical world.

Whether by coincidence or design, the Age of AI is also an age when organizations see themselves more accurately with a rich web of connections, with their choices and actions having more resonance than ever. That awareness is both a competitive tool, and a call to greater responsibility, potentially affording more customer loyalty and a more satisfied workforce for those who get it right.

That transformation won’t happen everywhere overnight, but it seems to be happening at all sorts of companies. And the trend for AI to assist, to be studied and grown, and to provide a richer understanding of the world, is happening every time someone touches this technology, at whatever level they need.

Have you ever heard of an optimization problem? Imagine you have a million marbles, all of different sizes, colors, patterns, and weights. You need to fill up 1,000 jars of different sizes with them, but each jar has restrictions as to which colors, patterns, and how many marbles of each type it can hold. After filling all the jars, you may keep any leftover marbles, so you want to ensure that these are the shiniest ones in the bunch. How do you go about solving this puzzle? There are many possibilities, but what would be the most efficient way to guarantee you’ll reach the best possible outcome every time? 

At HSBC, our Collateral Treasury desk and Collateral Management team have been solving a similar problem, but instead of marbles and jars, we work with around 50,000 assets that can be used as collateral, and 1,000s of collateral accounts.

Our Collateral Treasury Trading desk helps finance our Markets business by providing the required collateral, such as certain debt or equities, to cover its obligations to a client. The collateral could be used by HSBC for its margin requirements, CCPs (Central Counterparty Clearing Houses), or for securities financing transactions. Each obligation can have different eligibility criteria about what assets can be used as collateral for each client. These rules and restrictions revolve around the type of asset allowed or daily liquidity factors.

The process of matching collateral to obligations is known as an allocation, and it can get complicated the more diverse the collateral pool and the more customers you have. It is important to allocate collateral in the most efficient way and, traditionally, this business problem has been managed manually or by a third party against a set of simple rules.

But by collaborating with Google Cloud, we can improve collateral allocation through automation. Even small efficiency gains have the potential to make a significant difference when the amount of collateral inventory managed is tens of billions of dollars. It means the Collateral Treasury desk is much more efficient when managing its own funding costs or regulatory ratios such as the Liquidity Coverage Ratio (a key financial resource measure for banks that ensures sufficient high quality assets are readily available to survive periods of liquidity stress).

Leveraging AI to tackle a complex business problem

Our solution is OPTIC, a HSBC platform utilizing Google Operation Research Tools, or OR-Tools, an open-source optimization library provided by Google AI. Its main goal is to allow the Collateral Treasury desk to automate the collateral allocation process in the most optimal way on any given day. OPTIC’s architecture is based on microservices and provides the ability to handle large volumes of data, for which a scalable and self-managed infrastructure is needed.  OPTIC runs on Google Kubernetes Engine, which provides it with workload rebalancing, auto-scaling capabilities, and high availability. 

Additionally, we collaborated with Google Operations Research, which gave us access to the experience of Google AI engineers who were able to advise us on the best way to implement their optimization libraries to solve our business problem.  

We’ve found that using linear programming solvers such as Google OR-Tools is the best way to achieve the optimization capability that fundamentally changes how we manage our inventory. It enables us to optimize for multiple outcomes and be certain that we are doing this in the most efficient way possible. 

Finding the optimal way forward with automation

OPTIC works by consuming data feeds from a multitude of systems, then it standardizes the data, and combines with decision-making parameters and weightings Google OR-Tools can use, to understand and arrive at an optimal outcome. OPTIC can also provide insights and metrics that help optimize business decisions such as which assets can be added or removed in the future to make collateral allocation even more efficient.

Looking forward, this project makes us optimistic about using Google Kubernetes Engine to manage more of our microservices in other platforms. This will mean that we can scale without worrying about hardware or making big changes to our environment. With this solution, we’ll be able to mobilize and optimize our collateral according to our own view of the value and quality of the collateral, as well as the best fit for our exposures at any given time.  

Over time, the project can bring visible long-term benefits to HSBC’s Markets business, including decreased operational risks and potential to save significant funding costs. Next, we will continue to add new capabilities and data sources to our solution to continue solving some of the most complex business challenges in our industry.

There’s a lot we can learn from combining technology with science to help support the development of amazing discoveries. By using an AI system to predict protein shapes, we have the potential to accelerate research in every field of biology. Inside every cell in your body, billions of tiny molecular machines are hard at work. They are what allow your eyes to detect light, your neurons to fire, and the ‘instructions’ in your DNA to be read.

These intricate machines are known as proteins.

The protein folding puzzle

Protein folding is something that occurs naturally so that proteins become biologically functional, but it’s a complex process that sometimes fails. For decades, scientists have been trying to find a method to reliably predict a protein’s structure from its sequence of amino acids so we can better understand how proteins work.

The challenge? There are over 200 million known distinct proteins. Each one has a unique 3D shape that determines how it works and what it does. Because there are so many sequences and determining their 3-D structure experimentally is so time-consuming and expensive, scientists only know the exact structure of a tiny fraction of the proteins. And these experimental methods still fall far short of reliable statistical accuracy.

Deepmind’s gigantic leap

In 2020, Alphabet’s artificial intelligence research arm, DeepMind, made a massive breakthrough in predicting protein structures using a deep learning model called AlphaFold.

AlphaFold is trained on publicly available data consisting of about 170,000 protein structures, and is the first computational method that can regularly predict the 3D shape of a protein, at scale with a high degree of accuracy.

AlphaFold has already sent waves throughout the scientific community and has demonstrated the potential for AI to aid fundamental scientific discovery. Recently, Deepmind has made AlphaFold predictions available and open source to anyone. To date, more than 500,000 researchers from 190 countries have accessed the AlphaFold protein structure database to get closer to finding life-saving cures for diseases like Leishmaniasis and Chagas.

And now Deepmind has expanded the set of available predictions by more than 200 times (from nearly 1 million to nearly 214 million) to cover almost all cataloged proteins found in nature.

Open source predictions available on Google Cloud

Together, Google Cloud and Deepmind have released this dataset of predicted protein structures for plants, bacteria, animals, and other organisms as part of the Google Cloud Public Dataset program to enable bulk downloads at no cost. That means you can also create custom queries of the dataset using BigQuery!

Running AlphaFold on Google Cloud Vertex AI

Let’s say you want to run AlphaFold on your own in order to get protein structure predictions against your own set of data. There are a few challenges to keep in mind:

• You need to set up feature engineering against genetic sequence databases
• Preprocess data
• And run those inputs against pre-trained models

All of this requires allocating CPUs or GPUs, hosting a notebook environment, and scaling up for larger experiments. It’s hard to build and configure an on-premise system or cloud server to use AlphaFold whether you just want to try it out or run it at scale as a large organization.

That’s why we’re excited to share a deep integration between Google Cloud and Deepmind. On top of the Public Datasets program we have created end-to-end code samples for AlphaFold on Vertex AI, a managed end-to-end ML platform, to help address these challenges and speed up deployment. With AlphaFold on Vertex AI, you can manage a data science or machine learning workflow in a single development environment. You get access to pre-configured compute, storage, and end-to-end production notebooks. We have removed the heavy lifting needed to set up new ML environments, automate orchestration, and manage large clusters.

The AlphaFold inference workflow can be simplified with Vertex AI: from data preparation to feature engineering and deployment. Unlike the manual set up, the orchestrator makes it possible to parallelize steps, get predictions faster, and with better tracking.

Try it out first using Vertex AI Workbench

For those of you who want to try out a simplified version of AlphaFold, we have a Colab notebook that uses no templates (homologous structures) and a selected portion of the BFD database. You can deploy right on Vertex AI Workbench, which lets you specify a custom container image that we’ve already created for you. You’ll be able to:

1. Configure access to genetic databases
2. Configure GPU acceleration
3. Search against genetic databases
4. Use the pre-processed results as inputs to the AlphaFold model locally

In a little over an hour you can harness the power of AlphaFold to generate 3-D protein structures from amino acid sequences.

Run hundreds of experiments reliably using Vertex AI Pipelines

For organizations that want to run a full blown version of AlphaFold for many protein folding experiments a week, you’ll want an ML pipeline orchestrator. The AlphaFold Batch Inference solution is a set of code samples that uses Vertex AI Pipelines to support hundreds of concurrent inference pipelines with higher throughput to help you run experiments at scale. The solution uses Vertex AI Pipelines as an orchestrator and runtime, Vertex ML Metadata for metadata and artifacts, and Cloud Filestore to manage databases.

Because it’s built on Vertex AI Pipelines, you can automate, monitor, and experiment with interdependent parts of an ML workflow. The minimized inference elapsed times mean what normally would take you days, can now take you hours.

The solution includes two example pipelines:

1. The universal pipeline solution mirrors the exact logic in DeepMind’s open source inference script but decoupled into discrete tasks so you can run the same experiments faster, more efficiently, and with better tracking.

2. The customized pipeline solution shows you how to further optimize the inference workflow by parallelizing feature engineering steps so you can plug in your own database sources.

You get example components, pipelines, and notebooks to start, analyze, and recompile pipelines on different GPUs.

The AlphaFold Vertex AI Workbench solution is great for experimental use, while the AlphaFold Batch Inference solution on Vertex AI Pipelines is great for doing protein folding at scale with a strong process for reproducibility and tracking.

Now go forth and save the world!

Okay maybe that’s a bit hyperbolic, but this is inspiring stuff! What started as a 50 year challenge, to the discovery of AlphaFold, to being able to run it on Google Cloud, researchers, developers, and science enthusiasts now have access to one of the most pivotal advancements in the medical world. Even a non-specialist can easily use a Vertex AI notebook to exercise a simplified version of AlphaFold. The next answers to the mysteries of life and discovery of disease treatments have never felt more attainable. With these no-cost solutions to run AlphaFold on Vertex AI and the Public Dataset, you can help propel us in this worldwide endeavor.

Learn more about healthcare and life sciences solutions on Google Cloud here.

If you have feedback or want to share your experience with me, reach out to me at @stephr_wong.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report. 

First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax. 

Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:

  - invoke_document_ai:
    call: googleapis.documentai.v1.projects.locations.processors.batchProcess
    args:
        name: ${"projects/" + project + "/locations/eu/processors/" + processorId}
        location: "eu"
        body:
            inputDocuments:
                gcsPrefix:
                    gcsUriPrefix: ${bucket_input + report_id}
            documentOutputConfig:
                gcsOutputConfig: 
                    gcsUri: ${bucket_output + report_id}
            skipHumanReview: true
    result: document_ai_response

Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.

A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:

  - create_callback:
    call: events.create_callback_endpoint
    args:
        http_callback_method: "POST"
    result: callback_details
...
- await_callback:
    try:
        call: events.await_callback
        args:
            callback: ${callback_details}
            timeout: 3600
        result: callback_request
    except:
        as: e
        steps:
            - update_status_to_error:
              ...

In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.

Going further 

If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.