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Translate and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend

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You have Amazon Simple Storage Service (Amazon S3) buckets full of files containing incoming customer chats, product reviews, and social media feeds, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy thoughts or sad thoughts, translate their comments into a single common language, and create copies of the data for your business analysts with this new information added to each record. Additionally, you need to remove any personally identifiable information (PII), such as names, addresses, and credit card numbers.

You already know how to use Amazon Athena to transform data in Amazon S3 using simple SQL commands and the built-in functions in Athena. Now you can also use Athena to translate and analyze text fields, thanks to Amazon Translate, Amazon Comprehend, and the power of Athena User Defined Functions (UDFs).

Athena is an interactive query service that makes it easy to analyze data stored in Amazon S3 using SQL. Amazon Comprehend is a Natural Language Processing (NLP) service that makes it easy to uncover insights from text. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation. In this post, I show you how you can now use them together to perform the following actions:

  • Detect the dominant language of a text field
  • Detect the prevailing sentiment expressed—positive, negative, neither, or both
  • Detect or redact entities (such as items, places, or quantities)
  • Detect or redact PII
  • Translate text from one language to another

This post accomplishes the following goals:

  • Show you how to quickly set up the text analytics functions in your own AWS account (it’s fast and easy!)
  • Briefly explain how the functions work
  • Discuss performance and cost
  • Provide a tutorial where we do some text analytics on Amazon product reviews
  • Describe all the available functions

We include a list of all the available functions at the end of the post; the following code shows a few example queries and results:

USING FUNCTION detect_sentiment(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_sentiment('I am very happy', 'en') as sentiment sentiment POSITIVE USING FUNCTION detect_pii_entities(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_pii_entities('I am Bob, I live in Herndon VA, and I love cars', 'en') as pii pii [["NAME","Bob"],["ADDRESS","Herndon VA"]] USING FUNCTION redact_pii_entities(text_col VARCHAR, lang VARCHAR, type VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT redact_pii_entities('I am Bob, I live in Herndon VA, and I love cars', 'en', 'NAME,ADDRESS') as pii_redacted pii_redacted I am [NAME], I live in [ADDRESS], and I love cars USING FUNCTION translate_text(text_col VARCHAR, sourcelang VARCHAR, targetlang VARCHAR, terminologyname VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT translate_text('It is a beautiful day in the neighborhood', 'auto', 'fr', NULL) as translated_text translated_text C'est une belle journée dans le quartier

Install the text analytics UDF

An Athena UDF uses AWS Lambda to implement the function capability. I discuss more details later in this post, but you don’t need to understand the inner workings to use the text analytics UDF, so let’s get started.

Install the prebuilt Lambda function with the following steps:

  1. Navigate to the TextAnalyticsUDFHandler application in the AWS Serverless Application Repository.
  2. In the Application settings section, keep the settings at their defaults.
  3. Select I acknowledge that this app creates custom IAM roles.
  4. Choose Deploy.

And that’s it! Now you have a new Lambda function called textanalytics-udf. You’re ready to try some text analytics queries in Athena.

If you prefer to build and deploy from the source code instead, see the directions at the end of the GitHub repository README.

Run your first text analytics query

If you’re new to Athena, you may want to review the Getting Started guide.

As of this writing, the Athena UDF feature is still in preview. To enable it, create an Athena workgroup named AmazonAthenaPreviewFunctionality and run all the UDF queries from that workgroup.

Enter the following query into the SQL editor:

USING FUNCTION detect_sentiment(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_sentiment('I am very happy', 'en') as sentiment

You get a simple POSITIVE result. Now try again, varying the input text—try something less positive to see how the returned sentiment value changes.

To get the sentiment along with confidence scores for each potential sentiment value, use the following query instead:

USING FUNCTION detect_sentiment_all(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_sentiment_all('I am very happy', 'en') as sentiment

Now you get a JSON string containing the sentiment and all the sentiment scores:

{"sentiment":"POSITIVE","sentimentScore":{"positive":0.999519,"negative":7.407639E-5,"neutral":2.7478999E-4,"mixed":1.3210243E-4}}

You can use the built-in JSON extraction functions in Athena on this result to extract the fields for further analysis.

How the UDF works

For more information about the Athena UDF framework, see Querying with User Defined Functions.

The Java class TextAnalyticsUDFHandler implements our UDF Lambda function handler. Each text analytics function has a corresponding public method in this class.

Athena invokes our UDF Lambda function with batches of input records. The TextAnalyticsUDFHandler subdivides these batches into smaller batches of up to 25 rows to take advantage of the Amazon Comprehend synchronous multi-document batch APIs where they are available (for example, for detecting language, entities, and sentiment). When there is no synchronous multi-document API available (such as for DetectPiiEntity and TranslateText), we use the single-document API instead.

Amazon Comprehend API service quotas provide guardrails to limit your cost exposure from unintentional high usage (we discuss this more in the following section). By default, the multi-document batch APIs process up to 250 records per second, and the single-document APIs process up to 20 records per second. Our UDFs use exponential back off and retry to throttle the request rate to stay within these limits. You can request increases to the transactions per second quota for APIs using the Quota Request Template on the AWS Management Console.

Amazon Comprehend and Amazon Translate each enforce a maximum input string length of 5,000 utf-8 bytes. Text fields that are longer than 5,000 utf-8 bytes are truncated to 5,000 bytes for language and sentiment detection, and split on sentence boundaries into multiple text blocks of under 5,000 bytes for translation and entity or PII detection and redaction. The results are then combined.

Optimizing cost

In addition to Athena query costs, the text analytics UDF incurs usage costs from Lambda and Amazon Comprehend and Amazon Translate. The amount you pay is a factor of the total number of records and characters that you process with the UDF. For more information, see AWS Lambda pricing, Amazon Comprehend pricing, and Amazon Translate pricing.

To minimize the costs, I recommend that you avoid processing the same records multiple times. Instead, materialize the results of the text analytics UDF by using CREATE TABLE AS SELECT (CTAS) queries to capture the results in a separate table that you can then cost-effectively query as often as needed without incurring additional UDF charges. Process newly arriving records incrementally using INSERT INTO…SELECT queries to analyze and enrich only the new records and add them to the target table.

Avoid calling the text analytics functions needlessly on records that you will subsequently discard. Write your queries to filter the dataset first using temporary tables, views, or nested queries, and then apply the text analytics functions to the resulting filtered records.

Always assess the potential cost before you run text analytics queries on tables with vary large numbers of records.

In this section, we provide two example cost assessments.

Example 1: Analyze the language and sentiment of tweets

Let’s assume you have 10,000 tweet records, with average length 100 characters per tweet. Your SQL query detects the dominant language and sentiment for each tweet. You’re in your second year of service (the Free Tier no longer applies). The cost details are as follows:

  • Size of each tweet = 100 characters
  • Number of units (100 character) per record (minimum is 3 units) = 3
  • Total Units: 10,000 (records) x 3 (units per record) x 2 (Amazon Comprehend requests per record) = 60,000
  • Price per unit = $0.0001
  • Total cost for Amazon Comprehend = [number of units] x [cost per unit] = 60,000 x $0.0001 = $6.00 

Example 2: Translate tweets

Let’s assume that 2,000 of your tweets aren’t in your local language, so you run a second SQL query to translate them. The cost details are as follows:

  • Size of each tweet = 100 characters
  • Total characters: 2,000 (records) * 100 (characters per record) x 1 (Translate requests per record) = 200,000
  • Price per character = $0.000015
  • Total cost for Amazon Translate = [number of characters] x [cost per character] = 200,000 x $0.000015 = $3.00

Analyze insights from customer reviews

It’s time to put our new text analytics queries to use.

For a tutorial on getting actionable insights from customer reviews, see Tutorial: Analyzing Insights from Customer Reviews with Amazon Comprehend. This post provides an alternate approach to the same challenge: using SQL queries powered by Athena and Amazon Comprehend.

The tutorial takes approximately 10 minutes to complete, and costs up to $6 for Amazon Comprehend—there is no cost if you’re eligible for the Free Tier.

Create a new database in Athena

Run the following query in the Athena query editor:

CREATE DATABASE IF NOT EXISTS comprehendresults;

When connecting your data source, choose your new database.

Create a source table containing customer review data

We use the Amazon Customer Reviews Dataset, conveniently hosted for public access in Amazon S3.

  1. Run the following query in the Athena query editor:
    CREATE EXTERNAL TABLE amazon_reviews_parquet( marketplace string, customer_id string, review_id string, product_id string, product_parent string, product_title string, star_rating int, helpful_votes int, total_votes int, vine string, verified_purchase string, review_headline string, review_body string, review_date bigint, year int)
    PARTITIONED BY (product_category string)
    ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
    LOCATION 's3://amazon-reviews-pds/parquet/'
    

  1. Under Tables, find the new table amazon_reviews_parquet.
  2. From the options menu, choose Load partitions.
  1. Preview the new table, amazon_reviews_parquet.
  1. Run the following query to assess the average review length:
    SELECT AVG(LENGTH(review_body)) AS average_review_length FROM amazon_reviews_parquet

The average review length is around 365 characters. This equates to 4 Amazon Comprehend units per record (1 unit = 100 characters).

Detect the language for each review

To detect the language of each review, run the following query in the Athena query editor—it takes just over 1 minute to run and costs $2:

CREATE TABLE amazon_reviews_with_language WITH (format='parquet') AS
USING FUNCTION detect_dominant_language(col1 VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf')
SELECT *, detect_dominant_language(review_body) AS language
FROM amazon_reviews_parquet
LIMIT 5000

This query creates a new table, amazon_reviews_with_language, with one new column added: language. The LIMIT clause limits the number of records to 5,000.

Cost is calculated as: 5,000 (records) x 4 (units per record) x 1 (requests per record) x $0.0001 (Amazon Comprehend price per unit) = $2. 

Run the following query to see the detected language codes, with the corresponding count of reviews for each language:

SELECT language, count(*) AS count FROM amazon_reviews_with_language GROUP BY language ORDER BY count DESC

Detect sentiment and entities for each review

To detect sentiment, run the following query in the Athena query editor—it uses two text analytics functions, takes around 1 minute to run, and costs $4:

CREATE TABLE amazon_reviews_with_text_analysis WITH (format='parquet') AS
USING FUNCTION detect_sentiment_all(col1 VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf'), FUNCTION detect_entities_all(col1 VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf')
SELECT *, detect_sentiment_all(review_body, language) AS sentiment, detect_entities_all(review_body, language) AS entities
FROM amazon_reviews_with_language
WHERE language IN ('ar', 'hi', 'ko', 'zh-TW', 'ja', 'zh', 'de', 'pt', 'en', 'it', 'fr', 'es')

This query creates a new table, amazon_reviews_with_text_analysis, with two additional columns added: sentiment and entities. The WHERE clause restricts the result set to the list of languages supported by Amazon Comprehend sentiment and entity detection.

Cost is calculated as: 5,000 (records) x 4 (units per record) x 2 (requests per record) x $0.0001 (Amazon Comprehend price per unit) = $4.

Preview the new table and inspect some of the values for the new sentiment and entities columns. They contain JSON strings with nested structures and fields.

The following screenshot shows the sentiment column details.

The following screenshot shows the entities column details.

Next, we use the JSON functions in Athena to prepare these columns for analysis.

Prepare sentiment for analysis

Run the following SQL query to create a new table containing sentiment and sentiment scores expanded into separate columns:

CREATE TABLE sentiment_results_final WITH (format='parquet') AS
SELECT review_date, year, product_title, star_rating, language, CAST(JSON_EXTRACT(sentiment,'$.sentiment') AS VARCHAR) AS sentiment, CAST(JSON_EXTRACT(sentiment,'$.sentimentScore.positive') AS DOUBLE ) AS positive_score, CAST(JSON_EXTRACT(sentiment,'$.sentimentScore.negative') AS DOUBLE ) AS negative_score, CAST(JSON_EXTRACT(sentiment,'$.sentimentScore.neutral') AS DOUBLE ) AS neutral_score, CAST(JSON_EXTRACT(sentiment,'$.sentimentScore.mixed') AS DOUBLE ) AS mixed_score, review_headline, review_body
FROM amazon_reviews_with_text_analysis

Preview the new sentiment_results_final table (see the following screenshot). Does the sentiment generally align with the text of the review_body field? How does it correlate with the star_rating? If you spot any dubious sentiment assignments, check the confidence scores to see if the sentiment was assigned with a low confidence.

Prepare entities for analysis

Run the following SQL query to create a new table containing detected entities unnested into separate rows (inner subquery), with each field in a separate column (outer query):

CREATE TABLE entities_results_final WITH (format='parquet') AS
SELECT review_date, year, product_title, star_rating, language, CAST(JSON_EXTRACT(entity_element, '$.text') AS VARCHAR ) AS entity, CAST(JSON_EXTRACT(entity_element, '$.type') AS VARCHAR ) AS category, CAST(JSON_EXTRACT(entity_element, '$.score') AS DOUBLE ) AS score, CAST(JSON_EXTRACT(entity_element, '$.beginOffset') AS INTEGER ) AS beginoffset, CAST(JSON_EXTRACT(entity_element, '$.endOffset') AS INTEGER ) AS endoffset, review_headline, review_body
FROM
( SELECT * FROM ( SELECT *, CAST(JSON_PARSE(entities) AS ARRAY(json)) AS entities_array FROM amazon_reviews_with_text_analysis ) CROSS JOIN UNNEST(entities_array) AS t(entity_element)
)

Preview the contents of the new table, entities_results_final (see the following screenshot).

Visualize in Amazon QuickSight (optional)

As an optional step, you can visualize your results with Amazon QuickSight. For instructions, see Step 5: Visualizing Amazon Comprehend Output in Amazon QuickSight.

You can use the new word cloud visual type for entities, instead of tree map. In the word cloud chart menu, select Hide “other” categories.

You now have a dashboard with sentiment and entities visualizations that looks similar to the following screenshot.

Troubleshooting

If your query fails, check the Amazon CloudWatch metrics and logs generated by the UDF Lambda function.

  1. On the Lambda console, find the textanalytics-udf function.
  2. Choose Monitoring.

You can view the CloudWatch metrics showing how often the function ran, how long it runs for, how often it failed, and more.

  1. Choose View logs in CloudWatch to open the function log streams for additional troubleshooting insights.

For more information about viewing CloudWatch metrics via Lambda, see Using the Lambda console.

Additional use cases

There are many use cases for SQL text analytics functions. In addition to the example shown in this post, consider the following:

  • Simplify ETL pipelines by using incremental SQL queries to enrich text data with sentiment and entities, such as streaming social media streams ingested by Amazon Kinesis Data Firehose
  • Use SQL queries to explore sentiment and entities in your customer support texts, emails, and support cases
  • Prepare research-ready datasets by redacting PII from customer or patient interactions
  • Standardize many languages to a single common language

You may have additional use cases for these functions, or additional capabilities you want to see added, such as the following:

  • SQL functions to call custom entity recognition and custom classification models in Amazon Comprehend
  • SQL functions for de-identification—extending the entity and PII redaction functions to replace entities with alternate unique identifiers

Additionally, the implementation is open source, which means that you can clone the repo, modify and extend the functions as you see fit, and (hopefully) send us pull requests so we can merge your improvements back into the project and make it better for everyone.

Cleaning up

After you complete this tutorial, you might want to clean up any AWS resources you no longer want to use. Active AWS resources can continue to incur charges in your account.

  1. In Athena, run the following query to drop the database and all the tables:
    DROP DATABASE comprehendresults CASCADE

  1. In AWS CloudFormation, delete the stack serverlessrepo-TextAnalyticsUDFHandler.
  2. Cancel your QuickSight subscription.

Conclusion

I have shown you how to install the sample text analytics UDF Lambda function for Athena, so that you can use simple SQL queries to translate text using Amazon Translate, generate insights from text using Amazon Comprehend, and redact sensitive information. I hope you find this useful, and share examples of how you can use it to simplify your architectures and implement new capabilities for your business.

Please share your thoughts with us in the comments section, or in the issues section of the project’s GitHub repository.

Appendix: Available function reference

This section summarizes the functions currently provided. The README file provides additional details.

Detect language

This function uses the Amazon Comprehend BatchDetectDominantLanguage API to identify the dominant language based on the first 5,000 bytes of input text.

The following code returns a language code, such as fr for French or en for English:

USING FUNCTION detect_dominant_language(text_col VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_dominant_language('il fait beau à Orlando') as language

The following code returns a JSON formatted array of language codes and corresponding confidence scores:

USING FUNCTION detect_dominant_language_all(text_col VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_dominant_language_all('il fait beau à Orlando') as language_all

Detect sentiment

This function uses the Amazon Comprehend BatchDetectSentiment API to identify the sentiment based on the first 5,000 bytes of input text.

The following code returns a sentiment as POSITIVE, NEGATIVE, NEUTRAL, or MIXED:

USING FUNCTION detect_sentiment(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_sentiment('Joe is very happy', 'en') as sentiment

The following code returns a JSON formatted object containing detected sentiment and confidence scores for each sentiment value:

USING FUNCTION detect_sentiment_all(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_sentiment_all('Joe is very happy', 'en') as sentiment_all

Detect entities

This function uses the Amazon Comprehend DetectEntities API to identify PII. Input text longer than 5,000 bytes results in multiple Amazon Comprehend API calls.

The following code returns a JSON formatted object containing an array of entity types and values:

USING FUNCTION detect_entities(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_entities('His name is Joe, he lives in Richmond VA, he bought an Amazon Echo Show on January 5th, and he loves it', 'en') as entities

The following code returns a JSON formatted object containing an array of PII entity types, with their values, scores, and character offsets:

USING FUNCTION detect_entities_all(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_entities_all('His name is Joe, he lives in Richmond VA, he bought an Amazon Echo Show on January 5th, and he loves it', 'en') as entities_all

Redact entities

This function replaces entity values for the specified entity types with “[ENTITY_TYPE]”. Input text longer than 5,000 bytes results in multiple Amazon Comprehend API calls. See the following code:

USING FUNCTION redact_entities(text_col VARCHAR, lang VARCHAR, types VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT redact_entities('His name is Joe, he lives in Richmond VA, he bought an Amazon Echo Show on January 5th, and he loves it', 'en', 'ALL') as entities_redacted

The command returns a redacted version on the input string. Specify one or more entity types to redact by providing a comma-separated list of valid types in the types string parameter, or ALL to redact all types.

Detect PII

This function uses the DetectPiiEntities API to identify PII. Input text longer than 5,000 bytes results in multiple Amazon Comprehend API calls.

The following code returns a JSON formatted object containing an array of PII entity types and values:

USING FUNCTION detect_pii_entities(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_pii_entities('His name is Joe, his username is joe123 and he lives in Richmond VA', 'en') as pii

The following code returns a JSON formatted object containing an array of PII entity types, with their scores and character offsets:

USING FUNCTION detect_pii_entities_all(text_col VARCHAR, lang VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT detect_pii_entities_all('His name is Joe, his username is joe123 and he lives in Richmond VA', 'en') as pii_all

Redact PII

This function replaces the PII values for the specified PII entity types with “[PII_ENTITY_TYPE]”. Input text longer than 5,000 bytes results in multiple Amazon Comprehend API calls. See the following code:

USING FUNCTION redact_pii_entities(text_col VARCHAR, lang VARCHAR, types VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT redact_pii_entities('His name is Joe, his username is joe123 and he lives in Richmond VA', 'en', 'ALL') as pii_redacted

The function returns a redacted version on the input string. Specify one or more PII entity types to redact by providing a comma-separated list of valid types in the type string parameter, or ALL to redact all type.

Translate text

This function translates text from the source language to target language. Input text longer than 5,000 bytes results in multiple Amazon Translate API calls. See the following code:

USING FUNCTION translate_text(text_col VARCHAR, sourcelang VARCHAR, targetlang VARCHAR, customterminologyname VARCHAR) RETURNS VARCHAR TYPE LAMBDA_INVOKE WITH (lambda_name = 'textanalytics-udf') SELECT translate_text('It is a beautiful day in the neighborhood', 'auto', 'fr', NULL) as translated_text

The function returns the translated string. Optionally, auto-detect the source language (use auto as the language code, which uses Amazon Comprehend), and optionally specify a custom terminology (otherwise use NULL for customTerminologyName).


About the Author

Bob StrahanBob Strahan is a Principal Solutions Architect in the AWS Language AI Services team.

Source: https://aws.amazon.com/blogs/machine-learning/translate-and-analyze-text-using-sql-functions-with-amazon-athena-amazon-translate-and-amazon-comprehend/

Artificial Intelligence

Deep Learning vs Machine Learning: How an Emerging Field Influences Traditional Computer Programming

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When two different concepts are greatly intertwined, it can be difficult to separate them as distinct academic topics. That might explain why it’s so difficult to separate deep learning from machine learning as a whole. Considering the current push for both automation as well as instant gratification, a great deal of renewed focus has been heaped on the topic.

Everything from automated manufacturing worfklows to personalized digital medicine could potentially grow to rely on deep learning technology. Defining the exact aspects of this technical discipline that will revolutionize these industries is, however, admittedly much more difficult. Perhaps it’s best to consider deep learning in the context of a greater movement in computer science.

Defining Deep Learning as a Subset of Machine Learning

Machine learning and deep learning are essentially two sides of the same coin. Deep learning techniques are a specific discipline that belong to a much larger field that includes a large variety of trained artificially intelligent agents that can predict the correct response in an equally wide array of situations. What makes deep learning independent of all of these other techniques, however, is the fact that it focuses almost exclusively on teaching agents to accomplish a specific goal by learning the best possible action in a number of virtual environments.

Traditional machine learning algorithms usually teach artificial nodes how to respond to stimuli by rote memorization. This is somewhat similar to human teaching techniques that consist of simple repetition, and therefore might be thought of the computerized equivalent of a student running through times tables until they can recite them. While this is effective in a way, artificially intelligent agents educated in such a manner may not be able to respond to any stimulus outside of the realm of their original design specifications.

That’s why deep learning specialists have developed alternative algorithms that are considered to be somewhat superior to this method, though they are admittedly far more hardware intensive in many ways. Subrountines used by deep learning agents may be based around generative adversarial networks, convolutional neural node structures or a practical form of restricted Boltzmann machine. These stand in sharp contrast to the binary trees and linked lists used by conventional machine learning firmware as well as a majority of modern file systems.

Self-organizing maps have also widely been in deep learning, though their applications in other AI research fields have typically been much less promising. When it comes to defining the deep learning vs machine learning debate, however, it’s highly likely that technicians will be looking more for practical applications than for theoretical academic discussion in the coming months. Suffice it to say that machine learning encompasses everything from the simplest AI to the most sophisticated predictive algorithms while deep learning constitutes a more selective subset of these techniques.

Practical Applications of Deep Learning Technology

Depending on how a particular program is authored, deep learning techniques could be deployed along supervised or semi-supervised neural networks. Theoretically, it’d also be possible to do so via a completely unsupervised node layout, and it’s this technique that has quickly become the most promising. Unsupervised networks may be useful for medical image analysis, since this application often presents unique pieces of graphical information to a computer program that have to be tested against known inputs.

Traditional binary tree or blockchain-based learning systems have struggled to identify the same patterns in dramatically different scenarios, because the information remains hidden in a structure that would have otherwise been designed to present data effectively. It’s essentially a natural form of steganography, and it has confounded computer algorithms in the healthcare industry. However, this new type of unsupervised learning node could virtually educate itself on how to match these patterns even in a data structure that isn’t organized along the normal lines that a computer would expect it to be.

Others have proposed implementing semi-supervised artificially intelligent marketing agents that could eliminate much of the concern over ethics regarding existing deal-closing software. Instead of trying to reach as large a customer base as possible, these tools would calculate the odds of any given individual needing a product at a given time. In order to do so, it would need certain types of information provided by the organization that it works on behalf of, but it would eventually be able to predict all further actions on its own.

While some companies are currently relying on tools that utilize traditional machine learning technology to achieve the same goals, these are often wrought with privacy and ethical concerns. The advent of deep structured learning algorithms have enabled software engineers to come up with new systems that don’t suffer from these drawbacks.

Developing a Private Automated Learning Environment

Conventional machine learning programs often run into serious privacy concerns because of the fact that they need a huge amount of input in order to draw any usable conclusions. Deep learning image recognition software works by processing a smaller subset of inputs, thus ensuring that it doesn’t need as much information to do its job. This is of particular importance for those who are concerned about the possibility of consumer data leaks.

Considering new regulatory stances on many of these issues, it’s also quickly become something that’s become important from a compliance standpoint as well. As toxicology labs begin using bioactivity-focused deep structured learning packages, it’s likely that regulators will express additional concerns in regards to the amount of information needed to perform any given task with this kind of sensitive data. Computer scientists have had to scale back what some have called a veritable fire hose of bytes that tell more of a story than most would be comfortable with.

In a way, these developments hearken back to an earlier time when it was believed that each process in a system should only have the amount of privileges necessary to complete its job. As machine learning engineers embrace this paradigm, it’s highly likely that future developments will be considerably more secure simply because they don’t require the massive amount of data mining necessary to power today’s existing operations.

Image Credit: toptal.io

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Source: https://datafloq.com/read/deep-learning-vs-machine-learning-how-emerging-field-influences-traditional-computer-programming/13652

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Artificial Intelligence

Extra Crunch roundup: Tonal EC-1, Deliveroo’s rocky IPO, is Substack really worth $650M?

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For this morning’s column, Alex Wilhelm looked back on the last few months, “a busy season for technology exits” that followed a hot Q4 2020.

We’re seeing signs of an IPO market that may be cooling, but even so, “there are sufficient SPACs to take the entire recent Y Combinator class public,” he notes.

Once we factor in private equity firms with pockets full of money, it’s evident that late-stage companies have three solid choices for leveling up.

Seeking more insight into these liquidity options, Alex interviewed:

  • DigitalOcean CEO Yancey Spruill, whose company went public via IPO;
  • Latch CFO Garth Mitchell, who discussed his startup’s merger with real estate SPAC $TSIA;
  • Brian Cruver, founder and CEO of AlertMedia, which recently sold to a private equity firm.

After recapping their deals, each executive explains how their company determined which flashing red “EXIT” sign to follow. As Alex observed, “choosing which option is best from a buffet’s worth of possibilities is an interesting task.”

Thanks very much for reading Extra Crunch! Have a great weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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The Tonal EC-1

Image Credits: Nigel Sussman

On Tuesday, we published a four-part series on Tonal, a home fitness startup that has raised $200 million since it launched in 2018. The company’s patented hardware combines digital weights, coaching and AI in a wall-mounted system that sells for $2,995.

By any measure, it is poised for success — sales increased 800% between December 2019 and 2020, and by the end of this year, the company will have 60 retail locations. On Wednesday, Tonal reported a $250 million Series E that valued the company at $1.6 billion.

Our deep dive examines Tonal’s origins, product development timeline, its go-to-market strategy and other aspects that combined to spark investor interest and customer delight.

We call this format the “EC-1,” since these stories are as comprehensive and illuminating as the S-1 forms startups must file with the SEC before going public.

Here’s how the Tonal EC-1 breaks down:

We have more EC-1s in the works about other late-stage startups that are doing big things well and making news in the process.

What to make of Deliveroo’s rough IPO debut

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Let’s peek at the numbers and find out.

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The Exchange doubts many folks expected the IPO climate to get so chilly without warning. But we could be in for a Q2 pause in the formerly scorching climate for tech debuts.

Is Substack really worth $650M?

A $65 million Series B is remarkable, even by 2021 standards. But the fact that a16z is pouring more capital into the alt-media space is not a surprise.

Substack is a place where publications have bled some well-known talent, shifting the center of gravity in media. Let’s take a look at Substack’s historical growth.

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Robotic process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.

RPA has enabled executives to provide a level of automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.

E-commerce roll-ups are the next wave of disruption in consumer packaged goods

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This year is all about the roll-ups, the aggregation of smaller companies into larger firms, creating a potentially compelling path for equity value. The interest in creating value through e-commerce brands is particularly striking.

Just a year ago, digitally native brands had fallen out of favor with venture capitalists after so many failed to create venture-scale returns. So what’s the roll-up hype about?

Hack takes: A CISO and a hacker detail how they’d respond to the Exchange breach

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The cyber world has entered a new era in which attacks are becoming more frequent and happening on a larger scale than ever before. Massive hacks affecting thousands of high-level American companies and agencies have dominated the news recently. Chief among these are the December SolarWinds/FireEye breach and the more recent Microsoft Exchange server breach.

Everyone wants to know: If you’ve been hit with the Exchange breach, what should you do?

5 machine learning essentials nontechnical leaders need to understand

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Image Credits: David Malan (opens in a new window) / Getty Images

Machine learning has become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.

But for engineering and team leaders without an ML background, this can also feel overwhelming and intimidating.

Here are best practices and must-know components broken down into five practical and easily applicable lessons.

Embedded procurement will make every company its own marketplace

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Embedded procurement is the natural evolution of embedded fintech.

In this next wave, businesses will buy things they need through vertical B2B apps, rather than through sales reps, distributors or an individual merchant’s website.

Knowing when your startup should go all-in on business development

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There’s a persistent fallacy swirling around that any startup growing pain or scaling problem can be solved with business development.

That’s frankly not true.

Dear Sophie: What should I know about prenups and getting a green card through marriage?

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Dear Sophie:

I’m a founder of a startup on an E-2 investor visa and just got engaged! My soon-to-be spouse will sponsor me for a green card.

Are there any minimum salary requirements for her to sponsor me? Is there anything I should keep in mind before starting the green card process?

— Betrothed in Belmont

Startups must curb bureaucracy to ensure agile data governance

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Many organizations perceive data management as being akin to data governance, where responsibilities are centered around establishing controls and audit procedures, and things are viewed from a defensive lens.

That defensiveness is admittedly justified, particularly given the potential financial and reputational damages caused by data mismanagement and leakage.

Nonetheless, there’s an element of myopia here, and being excessively cautious can prevent organizations from realizing the benefits of data-driven collaboration, particularly when it comes to software and product development.

Bring CISOs into the C-suite to bake cybersecurity into company culture

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Cyber strategy and company strategy are inextricably linked. Consequently, chief information security officers in the C-Suite will be just as common and influential as CFOs in maximizing shareholder value.

How is edtech spending its extra capital?

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Edtech unicorns have boatloads of cash to spend following the capital boost to the sector in 2020. As a result, edtech M&A activity has continued to swell.

The idea of a well-capitalized startup buying competitors to complement its core business is nothing new, but exits in this sector are notable because the money used to buy startups can be seen as an effect of the pandemic’s impact on remote education.

But in the past week, the consolidation environment made a clear statement: Pandemic-proven startups are scooping up talent — and fast.

Tech in Mexico: A confluence of Latin America, the US and Asia

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Knowledge transfer is not the only trend flowing in the U.S.-Asia-LatAm nexus. Competition is afoot as well.

Because of similar market conditions, Asian tech giants are directly expanding into Mexico and other LatAm countries.

How we improved net retention by 30+ points in 2 quarters

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There’s certainly no shortage of SaaS performance metrics leaders focus on, but NRR (net revenue retention) is without question the most underrated metric out there.

NRR is simply total revenue minus any revenue churn plus any revenue expansion from upgrades, cross-sells or upsells. The greater the NRR, the quicker companies can scale.

5 mistakes creators make building new games on Roblox

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Even the most experienced and talented game designers from the mobile F2P business usually fail to understand what features matter to Robloxians.

For those just starting their journey in Roblox game development, these are the most common mistakes gaming professionals make on Roblox.

CEO Manish Chandra, investor Navin Chaddha explain why Poshmark’s Series A deck sings

CEO Manish Chandra, investor Navin Chaddha explain why Poshmark’s Series A deck sings image

“Lead with love, and the money comes.” It’s one of the cornerstone values at Poshmark. On the latest episode of Extra Crunch Live, Chandra and Chaddha sat down with us and walked us through their original Series A pitch deck.

Will the pandemic spur a smart rebirth for cities?

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Cities are bustling hubs where people live, work and play. When the pandemic hit, some people fled major metropolitan markets for smaller towns — raising questions about the future validity of cities.

But those who predicted that COVID-19 would destroy major urban communities might want to stop shorting the resilience of these municipalities and start going long on what the post-pandemic future looks like.

The NFT craze will be a boon for lawyers

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There’s plenty of uncertainty surrounding copyright issues, fraud and adult content, and legal implications are the crux of the NFT trend.

Whether a court would protect the receipt-holder’s ownership over a given file depends on a variety of factors. All of these concerns mean artists may need to lawyer up.

Viewing Cazoo’s proposed SPAC debut through Carvana’s windshield

It’s a reasonable question: Why would anyone pay that much for Cazoo today if Carvana is more profitable and whatnot? Well, growth. That’s the argument anyway.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://techcrunch.com/2021/04/02/extra-crunch-roundup-tonal-ec-1-deliveroos-rocky-ipo-is-substack-really-worth-650m/

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What did COVID do to all our models?

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What did COVID do to all our models?

An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.


By Heather Fyson, KNIME

What did COVID do to all our models?

After the KNIME Fall Summit, the dinosaurs went back home… well, switched off their laptops. Dean Abbott and John Elder, longstanding data science experts, were invited to the Fall Summit by Michael to join him in a discussion of The Future of Data Science: A Fireside Chat with Industry Dinosaurs. The result was a sparkling conversation about data science challenges and new trends. Since switching off the studio lights, Rosaria has distilled and expanded some of the highlights about change management, complexity, interpretability, and more in the data science world. Let’s see where it brought us.

What is your experience with change management in AI, when reality changes and models have to be updated? What did COVID do to all our models?

 
[Dean] Machine Learning (ML) algorithms assume consistency between past and future. When things change, the models fail. COVID has changed our habits, and therefore our data. Pre-COVID models struggle to deal with the new situation.

[John] A simple example would be the Traffic layer on Google Maps. After lockdowns hit country after country in 2020, Google Maps traffic estimates were very inaccurate for a while. It had been built on fairly stable training data but now that system was thrown completely out of whack.

How do you figure out when the world has changed and the models don’t work anymore?

 
[Dean] Here’s a little trick I use: I partition my data by time and label records as “before” and “after”. I then build a classification model to discriminate the “after” vs. the “before” from the same inputs the model uses. If the discrimination is possible, then the “after” is different from the “before”, the world has changed, the data has changed, and the models must be retrained.

How complicated is it to retrain models in projects, especially after years of customization?

 
[John] Training models is usually the easiest step of all! The vast majority of otherwise successful projects die in the implementation phase. The greatest time is spent in the data cleansing and preparation phase. And the most problems are missed or made in the business understanding / project definition phase. So if you understand what the flaw is and can obtain new data and have the implementation framework in place, creating a new model is, by comparison, very straightforward.

Based on your decades-long experience, how complex is it to put together a really functioning Data Science application?

 
[John] It can vary of course, by complexity. Most of our projects get functioning prototypes at least in a few months. But for all, I cannot stress enough the importance of feedback: You have to talk to people much more often than you want to. And listen! We learn new things about the business problem, the data, or constraints, each time. Not all us quantitative people are skilled at speaking with humans, so it often takes a team. But the whole team of stakeholders has to learn to speak the same language.

[Dean] It is important to talk to our business counterpart. People fear change and don’t want to change the current status. One key problem really is psychological. The analysts are often seen as an annoyance. So, we have to build the trust between the business counterpart and the analytics geeks. The start of a project should always include the following step: Sync up domain experts / project managers, the analysts, and the IT and infrastructure (DevOps) team so everyone is clear on the objectives of the project and how it will be executed. Analysts are number 11 on the top 10 list of people they have to see every day! Let’s avoid embodying data scientist arrogance: “The business can’t understand us/our techniques, but we know what works best”. What we don’t understand, however, are the domains experts are actually experts in the domain we are working in! Translation of data science assumptions and approaches into language that is understood by the domain experts is key!

The latest trend now is deep learning, apparently it can solve everything. I got a question from a student lately, asking “why do we need to learn other ML algorithms if deep learning is the state of the art to solve data science problems”?

 
[Dean] Deep learning sucked a lot of the oxygen out of the room. It feels so much like the early 1990s when neural networks ascended with similar optimism! Deep Learning is a set of powerful techniques for sure, but they are hard to implement and optimize. XGBoost, Ensembles of trees, are also powerful but currently more mainstream. The vast majority of problems we need to solve using advanced analytics really don’t require complex solutions, so start simple; deep learning is overkill in these situations. It is best to use the Occam’s razor principle: if two models perform the same, adopt the simplest.

About complexity. The other trend, opposite to deep learning, is ML interpretability. Here, you greatly (excessively?) simplify the model in order to be able to explain it. Is interpretability that important?

 
[John] I often find myself fighting interpretability. It is nice, sure, but often comes at too high a cost of the most important model property: reliable accuracy. But many stakeholders believe interpretability is essential, so it becomes a barrier for acceptance. Thus, it is essential to discover what kind of interpretability is needed. Perhaps it is just knowing what the most important variables are? That’s doable with many nonlinear models. Maybe, as with explaining to credit applicants why they were turned down, one just needs to interpret outputs for one case at a time? We can build a linear approximation for a given point. Or, we can generate data from our black box model and build an “interpretable” model of any complexity to fit that data.

Lastly, research has shown that if users have the chance to play with a model – that is, to poke it with trial values of inputs and see its outputs, and perhaps visualize it – they get the same warm feelings of interpretability. Overall, trust – in the people and technology behind the model – is necessary for acceptance, and this is enhanced by regular communication and by including the eventual users of the model in the build phases and decisions of the modeling process.

[Dean] By the way KNIME Analytics Platform has a great feature to quantify the importance of the input variables in a Random Forest! The Random Forest Learner node outputs the statistics of candidate and splitting variables. Remember that, when you use the Random Forest Learner node.

There is an increase in requests for explanations of what a model does. For example, for some security classes, the European Union is demanding verification that the model doesn’t do what it’s not supposed to do. If we have to explain it all, then maybe Machine Learning is not the way to go. No more Machine Learning?

 
[Dean]  Maybe full explainability is too hard to obtain, but we can achieve progress by performing a grid search on model inputs to create something like a score card describing what the model does. This is something like regression testing in hardware and software QA. If a formal proof what models are doing is not possible, then let’s test and test and test! Input Shuffling and Target Shuffling can help to achieve a rough representation of the model behavior.

[John] Talking about understanding what a model does, I would like to raise the problem of reproducibility in science. A huge proportion of journal articles in all fields — 65 to 90% — is believed to be unreplicable. This is a true crisis in science. Medical papers try to tell you how to reproduce their results. ML papers don’t yet seem to care about reproducibility. A recent study showed that only 15% of AI papers share their code.

Let’s talk about Machine Learning Bias. Is it possible to build models that don’t discriminate?

 
[John] (To be a nerd for a second, that word is unfortunately overloaded. To “discriminate” in the ML world word is your very goal: to make a distinction between two classes.) But to your real question, it depends on the data (and on whether the analyst is clever enough to adjust for weaknesses in the data): The models will pull out of the data the information reflected therein. The computer knows nothing about the world except for what’s in the data in front of it. So the analyst has to curate the data — take responsibility for those cases reflecting reality. If certain types of people, for example, are under-represented then the model will pay less attention to them and won’t be as accurate on them going forward. I ask, “What did the data have to go through to get here?” (to get in this dataset) to think of how other cases might have dropped out along the way through the process (that is survivor bias). A skilled data scientist can look for such problems and think of ways to adjust/correct for them.

[Dean] The bias is not in the algorithms. The bias is in the data. If the data is biased, we’re working with a biased view of the world. Math is just math, it is not biased.

Will AI take over humanity?!

 
[John] I believe AI is just good engineering. Will AI exceed human intelligence? In my experience anyone under 40 believes yes, this is inevitable, and most over 40 (like me, obviously): no! AI models are fast, loyal, and obedient. Like a good German Shepherd dog, an AI model will go and get that ball, but it knows nothing about the world other than the data it has been shown. It has no common sense. It is a great assistant for specific tasks, but actually quite dimwitted.

[Dean] On that note, I would like to report two quotes made by Marvin Minsky in 1961 and 1970, from the dawn of AI, that I think describe well the future of AI.

“Within our lifetime some machines may surpass us in general intelligence” (1961)

“In three to eight years we’ll have a machine with the intelligence of a human being” (1970)

These ideas have been around for a long time. Here is one reason why AI will not solve all the problems: We’re judging its behavior based on one number, one number only! (Model error.) For example, predictions of stock prices over the next five years, predicted by building models using root mean square error as the error metric, cannot possibly paint the full picture of what the data are actually doing and severely hampers the model and its ability to flexibly uncover the patterns. We all know that RMSE is too coarse of a measure. Deep Learning algorithms will continue to get better, but we also need to get better at judging how good a model really is. So, no! I do not think that AI will take over humanity.

We have reached the end of this interview. We would like to thank Dean and John for their time and their pills of knowledge. Let’s hope we meet again soon!

About Dean Abbott and John Elder

What did COVID do to all our models Dean Abbott is Co-Founder and Chief Data Scientist at SmarterHQ. He is an internationally recognized expert and innovator in data science and predictive analytics, with three decades of experience solving problems in omnichannel customer analytics, fraud detection, risk modeling, text mining & survey analysis. Included frequently in lists of pioneering data scientists and data scientists, he is a popular keynote speaker and workshop instructor at conferences worldwide, also serving on Advisory Boards for the UC/Irvine Predictive Analytics and UCSD Data Science Certificate programs. He is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).


What did COVID do to all our models John Elder founded Elder Research, America’s largest and most experienced data science consultancy in 1995. With offices in Charlottesville VA, Baltimore MD, Raleigh, NC, Washington DC, and London, they’ve solved hundreds of challenges for commercial and government clients by extracting actionable knowledge from all types of data. Dr. Elder co-authored three books — on practical data mining, ensembles, and text mining — two of which won “book of the year” awards. John has created data mining tools, was a discoverer of ensemble methods, chairs international conferences, and is a popular workshop and keynote speaker.


 
Bio: Heather Fyson is the blog editor at KNIME. Initially on the Event Team, her background is actually in translation & proofreading, so by moving to the blog in 2019 she has returned to her real passion of working with texts. P.S. She is always interested to hear your ideas for new articles.

Original. Reposted with permission.

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Source: https://www.kdnuggets.com/2021/04/covid-do-all-our-models.html

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The AI Trends Reshaping Health Care

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Click to learn more about author Ben Lorica.

Applications of AI in health care present a number of challenges and considerations that differ substantially from other industries. Despite this, it has also been one of the leaders in putting AI to work, taking advantage of the cutting-edge technology to improve care. The numbers speak for themselves: The global AI in health care market size is expected to grow from $4.9 billion in 2020 to $45.2 billion by 2026. Some major factors driving this growth are the sheer volume of health care data and growing complexities of datasets, the need to reduce mounting health care costs, and evolving patient needs.

Deep learning, for example, has made considerable inroads into the clinical environment over the last few years. Computer vision, in particular, has proven its value in medical imaging to assist in screening and diagnosis. Natural language processing (NLP) has provided significant value in addressing both contractual and regulatory concerns with text mining and data sharing. Increasing adoption of AI technology by pharmaceutical and biotechnology companies to expedite initiatives like vaccine and drug development, as seen in the wake of COVID-19, only exemplifies AI’s massive potential.

We’re already seeing amazing strides in health care AI, but it’s still the early days, and to truly unlock its value, there’s a lot of work to be done in understanding the challenges, tools, and intended users shaping the industry. New research from John Snow Labs and Gradient Flow, 2021 AI in Healthcare Survey Report, sheds light on just this: where we are, where we’re going, and how to get there. The global survey explores the important considerations for health care organizations in varying stages of AI adoption, geographies, and technical prowess to provide an extensive look into the state of AI in health care today.               

One of the most significant findings is around which technologies are top of mind when it comes to AI implementation. When asked what technologies they plan to have in place by the end of 2021, almost half of respondents cited data integration. About one-third cited natural language processing (NLP) and business intelligence (BI) among the technologies they are currently using or plan to use by the end of the year. Half of those considered technical leaders are using – or soon will be using – technologies for data integration, NLP, business intelligence, and data warehousing. This makes sense, considering these tools have the power to help make sense of huge amounts of data, while also keeping regulatory and responsible AI practices in mind.

When asked about intended users for AI tools and technologies, over half of respondents identified clinicians among their target users. This indicates that AI is being used by people tasked with delivering health care services – not just technologists and data scientists, as in years past. That number climbs even higher when evaluating mature organizations, or those that have had AI models in production for more than two years. Interestingly, nearly 60% of respondents from mature organizations also indicated that patients are also users of their AI technologies. With the advent of chatbots and telehealth, it will be interesting to see how AI proliferates for both patients and providers over the next few years.

In considering software for building AI solutions, open-source software (53%) had a slight edge over public cloud providers (42%). Looking ahead one to two years, respondents indicated openness to also using both commercial software and commercial SaaS. Open-source software gives users a level of autonomy over their data that cloud providers can’t, so it’s not a big surprise that a highly regulated industry like health care would be wary of data sharing. Similarly, the majority of companies with experience deploying AI models to production choose to validate models using their own data and monitoring tools, rather than evaluation from third parties or software vendors. While earlier-stage companies are more receptive to exploring third-party partners, more mature organizations are tending to take a more conservative approach.                      

Generally, attitudes remained the same when asked about key criteria used to evaluate AI solutions, software libraries or SaaS solutions, and consulting companies to work with.Although the answers varied slightly for each category,technical leaders considered no data sharing with software vendors or consulting companies, the ability to train their own models, and state-of-the art accuracy as top priorities. Health care-specific models and expertise in health care data engineering, integration, and compliance topped the list when asked about solutions and potential partners. Privacy, accuracy, and health care experience are the forces driving AI adoption. It’s clear that AI is poised for even more growth, as data continues to grow and technology and security measures improve. Health care, which can sometimes be seen as a laggard for quick adoption, is taking to AI and already seeing its significant impact. While its approach, the top tools and technologies, and applications of AI may differ from other industries, it will be exciting to see what’s in store for next year’s survey results.

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Source: https://www.dataversity.net/the-ai-trends-reshaping-health-care/

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