When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML. But what can you do if you don’t know what kind of anomaly to expect, and you don’t have labeled data? Unlike typical predictive techniques that leverage supervised learning, organizations may need to be able to detect anomalies in the absence of labeled data. 

Today we are announcing the public preview of new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data. Depending on whether or not the training data is time series, users can now detect anomalies in training data or on new input data using a new ML.DETECT_ANOMALIES function (documentation), with the following models:

• Autoencoder model, now in Public Preview (documentation)
• K-means model, already GA (documentation)
• ARIMA_PLUS time series model, already GA (documentation)

How does anomaly detection with ML.DETECT_ANOMALIES work?

To detect anomalies in non-time-series data, you can use:

• K-means clustering models: When you use ML.DETECT_ANOMALIES with a k-means model, anomalies are identified based on the value of each input data point’s normalized distance to its nearest cluster. If that distance exceeds a threshold determined by the contamination value provided by the user, the data point is identified as an anomaly. 
• Autoencoder models: When you use ML.DETECT_ANOMALIES with an autoencoder model, anomalies are identified based on the reconstruction error for each data point. If the error exceeds a threshold determined by the contamination value, it is identified as an anomaly. 

To detect anomalies in time-series data, you can use: 

• ARIMA_PLUS time series models: When you use ML.DETECT_ANOMALIES with an ARIMA_PLUS model, anomalies are identified based on the confidence interval for that timestamp. If the probability that the data point at that timestamp occurs outside of the prediction interval exceeds a probability threshold provided by the user, the datapoint is identified as an anomaly.

Below we show code examples of anomaly detection in BigQuery ML for each of the above scenarios.

Anomaly detection with a k-means clustering model

You can now detect anomalies using k-means clustering models, by running ML.DETECT_ANOMALIES to detect anomalies in the training data or in new input data. Begin by creating a k-means clustering model:

Language: SQL

  CREATE MODEL `mydataset.my_kmeans_model`
OPTIONS(
  MODEL_TYPE = 'kmeans',
  NUM_CLUSTERS = 8,
  KMEANS_INIT_METHOD = 'kmeans++'
) AS
SELECT 
  * EXCEPT(Time, Class) 
FROM 
  `bigquery-public-data.ml_datasets.ulb_fraud_detection`;

With the k-means clustering model trained, you can now run ML.DETECT_ANOMALIES to detect anomalies in the training data or in new input data.
To detect anomalies in the training data, use ML.DETECT_ANOMALIES with the same data used during training:

Language: SQL

  SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_kmeans_model`,
                      STRUCT(0.02 AS contamination),
                      TABLE `bigquery-public-data.ml_datasets.ulb_fraud_detection`);

To detect anomalies in new data, use ML.DETECT_ANOMALIES and provide new data as input:

Language: SQL

  SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_kmeans_model`,
                      STRUCT(0.02 AS contamination),
                      (SELECT * FROM `mydataset.newdata`));

How does anomaly detection work for k-means clustering models? 

Anomalies are identified based on the value of each input data point’s normalized distance to its nearest cluster, which, if exceeds a threshold determined by the contamination value, is identified as an anomaly. How does this work exactly? With a k-means model and data as inputs, ML.DETECT_ANOMALIES first computes the absolute distance for each input data point to all cluster centroids in the model, then normalizes each distance by the respective cluster radius (which is defined as the standard deviation of the absolute distances of all points in this cluster to the centroid). For each data point, ML.DETECT_ANOMALIES returns the nearest centroid_id based on normalized_distance, as seen in the screenshot above. The contamination value, specified by the user, determines the threshold of whether a data point is considered an anomaly. For example, a contamination value of 0.1 means that the top 10% of descending normalized distance from the training data will be used as the cut-off threshold. If the normalized distance for a datapoint exceeds the threshold, then it is identified as an anomaly. Setting an appropriate contamination will be highly dependent on the requirements of the user or business. 

For more information on anomaly detection with k-means clustering, please see the documentation here.

Anomaly detection with an autoencoder model

You can now detect anomalies using autoencoder models, by running ML.DETECT_ANOMALIES to detect anomalies in the training data or in new input data. 

Begin by creating an autoencoder model:

Language: SQL

  CREATE MODEL `mydataset.my_autoencoder_model`
OPTIONS(
  model_type='autoencoder',
  activation_fn='relu',
  batch_size=8,
  dropout=0.2,  
  hidden_units=[32, 16, 4, 16, 32],
  learn_rate=0.001,
  l1_reg_activation=0.0001,
  max_iterations=10,
  optimizer='adam'
) AS 
SELECT 
  * EXCEPT(Time, Class) 
FROM 
  `bigquery-public-data.ml_datasets.ulb_fraud_detection`;

To detect anomalies in the training data, use ML.DETECT_ANOMALIES with  the same data used during training:

Language: SQL

  SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_autoencoder_model`,
                      STRUCT(0.02 AS contamination),
                      TABLE `bigquery-public-data.ml_datasets.ulb_fraud_detection`);

To detect anomalies in new data, use ML.DETECT_ANOMALIES and provide new data as input:

Language: SQL

  SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_autoencoder_model`,
                      STRUCT(0.02 AS contamination),
                      (SELECT * FROM `mydataset.newdata`));

How does anomaly detection work for autoencoder models? 

Anomalies are identified based on the value of each input data point’s reconstructed error, which, if exceeds a threshold determined by the contamination value, is identified as an anomaly. How does this work exactly? With an autoencoder model and data as inputs, ML.DETECT_ANOMALIES first computes the mean_squared_error for each data point between its original values and its reconstructed values. The contamination value, specified by the user, determines the threshold of whether a data point is considered an anomaly. For example, a contamination value of 0.1 means that the top 10% of descending error from the training data will be used as the cut-off threshold. Setting an appropriate contamination will be highly dependent on the requirements of the user or business. 

For more information on anomaly detection with autoencoder models, please see the documentation here.

Anomaly detection with an ARIMA_PLUS time-series model

With ML.DETECT_ANOMALIES, you can now detect anomalies using ARIMA_PLUS time series models in the (historical) training data or in new input data. Here are some examples of when might you want to detect anomalies with time-series data:

Detecting anomalies in historical data: 

• Cleaning up data for forecasting and modeling purposes, e.g. preprocessing historical time series before using them to train an ML model.  
• When you have a large number of retail demand time series (thousands of products across hundreds of stores or zip codes), you may want to quickly identify which stores and product categories had anomalous sales patterns, and then perform a deeper analysis of why that was the case.

Forward looking anomaly detection: 

• Detecting consumer behavior and pricing anomalies as early as possible: e.g. if traffic to a specific product page suddenly and unexpectedly spikes, it might be because of an error in the pricing process that leads to an unusually low price. 
• When you have a large number of retail demand time series (thousands of products across hundreds of stores or zip codes), you would like to identify which stores and product categories had anomalous sales patterns based on your forecasts, so you can quickly respond to any unexpected spikes or dips.

How do you detect anomalies using ARIMA_PLUS? Begin by creating an ARIMA_PLUS time series model:

Language: SQL

  CREATE OR REPLACE MODEL mydataset.my_arima_plus_model
OPTIONS(
  MODEL_TYPE='ARIMA_PLUS',
  TIME_SERIES_TIMESTAMP_COL='date',
  TIME_SERIES_DATA_COL='total_amount_sold',
  TIME_SERIES_ID_COL='item_name',
  HOLIDAY_REGION='US' 
) AS
SELECT
  date,
  item_description AS item_name,
  SUM(bottles_sold) AS total_amount_sold
FROM
  `bigquery-public-data.iowa_liquor_sales.sales`
GROUP BY
  date,
  item_name
HAVING
  date BETWEEN DATE('2016-01-04') AND DATE('2017-06-01')
  AND item_name IN ("Black Velvet", "Captain Morgan Spiced Rum",
    "Hawkeye Vodka", "Five O'Clock Vodka", "Fireball Cinnamon Whiskey");

To detect anomalies in the training data, use ML.DETECT_ANOMALIES with the model obtained above:

Language: SQL

  SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_arima_plus_model`,
                      STRUCT(0.8 AS anomaly_prob_threshold));

To detect anomalies in new data, use ML.DETECT_ANOMALIES and provide new data as input:

Language: SQL

  WITH
  new_data AS (
  SELECT
    date,
    item_description AS item_name,
    SUM(bottles_sold) AS total_amount_sold
  FROM
    `bigquery-public-data.iowa_liquor_sales.sales`
  GROUP BY
    date,
    item_name
  HAVING
    date BETWEEN DATE('2017-06-02')
    AND DATE('2017-10-01')
    AND item_name IN ('Black Velvet',
      'Captain Morgan Spiced Rum',
      'Hawkeye Vodka',
      "Five O'Clock Vodka",
      'Fireball Cinnamon Whiskey') )
SELECT
  *
FROM
  ML.DETECT_ANOMALIES(MODEL `mydataset.my_arima_plus_model`,
    STRUCT(0.8 AS anomaly_prob_threshold),
    (SELECT
      *
    FROM
      new_data));

For more information on anomaly detection with ARIMA_PLUS time series models, please see the documentation here.

Thanks to the BigQuery ML team, especially Abhinav Khushraj, Abhishek Kashyap, Amir Hormati, Jerry Ye, Xi Cheng, Skander Hannachi, Steve Walker, and Stephanie Wang.

With a growing fleet of 325 aircraft that cover more than 1,000 routes across 158 airports, easyJet is one of Europe’s most popular airlines. And easyJet serves an average of 90 million passengers each year, so a helpful mobile experience for its customers is a top priority.

Travellers today are inherently mobile-first, so finding new ways to make it easier for them to search and book flights is key. To do exactly that, easyJet partnered with technology company Travelport to develop Speak Now, a new feature on easyJet’s mobile app that interprets voice searches to deliver accurate and relevant flight information to travelers.

See how it works:

Powered by Dialogflow, Google Cloud’s natural language understanding tool for building conversational experiences, Speak Now lets customers ask questions to determine exactly what they’re looking for—from destinations, to dates and times, to airports they want to fly from. 

How do we create conversational experiences across devices and platforms for enterprises?
It’s clear that the rise in voice search is changing the way we go about our daily lives. Twenty-seven percent of the global online population already uses voice search on mobile, and the rapid adoption of this technology is reshaping entire industries. It’s no surprise that easyJet looked to adopt this technology to positively transform experiences for their customers.   

Dialogflow, a core component of Google Cloud Contact Center AI, makes it easy to build accurate, flexible conversation interfaces that allow users to ask questions and accomplish tasks in everyday language. It understands the nuances of human language and translates end-user text or audio during a conversation to structured data that apps and services can understand. 

Speak Now is a great example of how we’re using cloud technologies and AI to make the experience of buying and managing travel continually better for everyone.

Daniel Young, Head of Digital Experience, easyJet

Daniel Young, Head of Digital Experience at easyJet commented: “We picked Dialogflow due to its strengths and ease with which a powerful conversational agent can be built. Speak Now is a great example of how we’re using cloud technologies and AI to make the experience of buying and managing travel continually better for everyone. This is the latest in a series of innovative features that will make booking travel as easy as it can possibly be, giving easyJet customers a helpful digital experience.”

Consumers already rely on voice assistants to play their favorite music, add items to a shopping list, and order taxis, Speak Now is a great example of how voice assistants can now make the customer experience better and more intuitive for travel.

Be it marketing, sales, or even security, most departments in large organizations today have a range of SaaS tools at their disposal to help make their work more efficient. But people in corporate finance and accounting have been underserved in that respect. Their days are still spent on routine, mundane tasks that keep them away from more stimulating work. Auditoria.AI aims to change that by automating those functions with AI and natural language technology.

We strive to improve the lives of finance and accounting professionals by automating the routine, repetitive, and laborious parts of the finance function, such as copy-and-pasting data, validating documents, and checking for errors on spreadsheets, freeing these teams to focus instead on providing valuable, strategic insights to the business. 

To that end, the problems we’re solving affect three major finance functions:

• Accounts Payable, responsible for sending money out of the company, such as bill payments. 
• Accounts Receivable, responsible for bringing money into the company, such as invoicing for services. 
• General accounting, a broader term consisting of functions of the general ledger team and the CFO, including closing books. 

Historically, making these processes more efficient entailed dedicating more personnel to them. But this didn’t necessarily mean more work was done faster and to the highest standards. Many finance professionals are often overworked, dedicating extra hours, weekends, and sometimes holidays to process invoices, collect payments, and close the books on time. 

We created solutions for these three finance functions, with our SmartBots taking care of the back-and-forth communications between finance teams, vendors, and customers. In large companies, these micro-transactions add up to thousands per day, resulting in finance professionals spending entire days reading inquiries, interpreting requests, looking for relevant information, and answering as many as possible. But with our AR helpdesk, for example, accounts receivable tasks, such as a request for a copy of an invoice, get automated. Our technology reads emails and attachments to understand what is being requested. Then it connects to the Enterprise Resource Planning (ERP) software to grab the relevant information and attach it to the email, so the recipient gets a response within 60 seconds. 

Building the smart assistant that finance teams need

In our automation flow, we constantly handle different types of documents, from invoices and tax forms to receipts and email messages. But processing the interaction between computers and human language is complex. You must detect intent and facts, and understand the context before finding the specific slots of information extraction that may be relevant to specific processes and requests. Our solution adds value by extracting the right information in the right context, from the right document, for the relevant finance function. Instead of building everything from scratch, we turned to Google Cloud’s Document AI to support extracting data from unstructured documents to understand and analyze them. 

Document AI comes with pre-built models that help analyze specific parts of our post-production lifecycle. For example, Invoice Parser extracts text and values from invoices, including invoice number, supplier name, invoice amount, tax amount, and invoice due date, all of which are necessary for our SmartBots to execute an extraction workflow. These out-of-the-box features significantly accelerate our own product development process and time-to-market, which are critical for the performance of a startup such as ours. 

To ensure a high quality of information extraction, we used to do document readings in-house. Having automated some of that with Document AI, we’re at 85% accuracy extracting files, and with some additional customization efforts, we will achieve 95%+ extraction accuracy.

Meanwhile, we have now streamlined internal processes, which ultimately translates into faster services for our customers. For example, assuming all the information has been provided, it generally took up to 15 minutes to process a tax form. We now do that in seconds. 

The value of automation doesn’t stop there. Using DocumentAI to automate structured data extraction from documents, we have managed to:

• Speed up the collection of general ledger entries by 90%+
• Reduce errors and omissions by 85%+
• Close books 20% faster
• Improved the productivity of full-time employees by 60%+
• Reduce process workload by 75%+
• Improve vendor serviceability by 75%+
• Reduce vendor risk and fraud by 50%+

Leveraging automation to focus on more innovation

Automating some of our processes with Document AI also means we have more time to focus on developing new features and further improving our solution. 80-90% of the time used for extracting custom fields from documents has now been automated with an OCR metadata library. 

With Document AI taking care of standard extraction, we focus on the intelligence we add to post-extraction. For example, when an invoice comes in from a vendor, our application needs to figure out which vendor it is to match it to the correct records in the ERP. But variations in the documents could interfere with that extraction process. The vendor’s trading name might be slightly different from the company’s name registered in our internal system, delaying the process. With more time on our hands, we’re now working on features enabling our models to leverage logos and other elements extracted from documents to swiftly match them to the correct company registered in our systems.  

With the benefits we’ve seen thus far, we look forward to accelerating our international growth. We’ll be relying on Google Cloud’s Document AI to automate operations, potentially in different languages, as we continually remove friction from the work lives of finance and accounting people worldwide.

Over the decades, technology has helped us organize large amounts of physical information in ways that are streamlined, efficient, and easily accessible. Rows upon rows of encyclopedias are no longer needed; simply punch in or speak a query into Google Search and find numerous results at your fingertips. There’s no need to haul around cases of CDs or cassettes either, when you can access hundreds of thousands of songs on music streaming services. 

We wanted to bring that same level of accessibility to one specific type of publication: the printed car manual. Here’s how we shifted the paper manual to become an easy-to-access, voice-activated digital experience for owners of the all-new Sienna. It’s helped this resource become just as modern and useful as Toyotas themselves.

Putting cloud technology in the driver’s seat

Far too often, the printed car manual remains unused, collecting dust in the glove compartment—that is, until it’s needed during a roadside emergency or to solve the meaning of a mysterious light popping up on the dashboard. Even then, thumbing through hundreds of pages during high-stakes moments can be stressful. On top of that, these manuals can be expensive to print and update.

Digital versions, such as a PDF file, are a nice start. But, they’re often little more than reformatted flat documents. In poor visual conditions on the side of a road, the last thing a driver wants to do is squint at a phone or scroll through pages of tiny text. And similar to printed manuals, digital versions don’t facilitate ongoing, real-time conversations between drivers and automakers when it matters the most; they’re often only text and pictures, and at best, schematic drawings.

With that in mind, we set out to elevate and personalize the car manual by creating a voice-activated digital owner’s manual experience, all powered by Google Cloud. A voice-based assistant was the clear choice because, as it felt like the most intuitive, natural option, especially while driving. In fact, 51% of U.S. adults have used a voice assistant while driving, and 95% of all drivers expect to use a voice assistant in the next three years. 

We’re now putting this technology on the road by powering the Toyota Driver’s Companion for the all-new 2021 Toyota Sienna model. Accessible through the existing Toyota app, this companion provides real-time assistance, any time of the day. Drivers can ask questions and the companion will efficiently provide helpful answers in convenient ways, from voice to 3D walkthroughs and explorable environments. 

What a modern car manual should do

The Toyota Driver’s Companion has interactive features to help drivers discover the Sienna’s dashboard, set up the car’s interior and exterior appearance, better understand vehicle maintenance, and explore Dynamic Radar Cruise Control, Lane Departure Alert and other features. 

Here are a few other additional key features to call out within the Toyota Driver’s Companion: 

• An easily accessible virtual voice through the Toyota Driver’s Companion lets app users ask personal questions about their 2021 Sienna such as “what’s the height of my car?” and receive immediate answers either by voice, display or interactive input. 
• The manual automatically connects with the purchased vehicle’s VIN number to create a completely personalized experience, curated specifically for the driver. For example, if an unfamiliar light on the dashboard pops up, the Toyota Driver’s Companion can help identify the light’s meaning.
• Interactive hotspots throughout the vehicle’s interior let drivers explore the cabin virtually. Drivers can discover button functionalities, find specific dials, and learn more about car functions, such as how to slide seats or open doors, to become acclimated with their new vehicle.

To bring this experience to life, we tapped into some of our key Google Cloud solutions:

• APIs powered by Google Cloud artificial intelligence technology make accessing specific vehicle information easy and effortless, by leveraging Google’s natural language processing:
  • Google Cloud DialogFlow API serves as the decision tree that gives intelligence for both finding an answer for a question, i.e., how the Companion responds to the end user’s questions. 
  • One of our Text-to-Speech APIs—called Wavenet—creates the Companion’s realistic voice. 
  • And finally, our Speech-to-Text API “listens” to the user’s voice and finds the correct information to craft responses. That means a driver can ask a question multiple ways, and the Companion will still respond with the right answer. 

Our Firebase mobile app dev platform gleans analytic insights that help improve the overall experience and services for OEMs and drivers alike.https://www.youtube.com/embed/66QxWS-PzIM?enablejsapi=1&

We’re encouraging better consumer experiences by providing faster access to fresh information, in a natural, accessible format—voice. But these new voice-activated experiences aren’t only an opportunity to help out drivers; it’s also about strengthening connections between drivers, their vehicles, and automakers, too. 

Our hope is that through this information exchange, drivers can provide feedback on the most frequently misunderstood features, enabling OEMs to address questions early on. By understanding the most requested features, OEMs can also predict and inform driver questions about features. We’re incredibly excited to help make the driver’s experience more connected and helpful.