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Google recently published two new algorithms related to natural language processing. One of them claims a new state of the art for understanding how to answer questions. Google has a longstanding goal to transform search into a computer that can understand questions and answer them like a human. Google itself says these algorithms are just the beginning.

Just Because Google Publishes Research…

It’s important to note that Google’s boilerplate stance on research papers and patents can be paraphrased thusly, “Just because Google publishes a research paper or patent does not mean that Google is actually using it.”

Understanding Questions by the Answers

The first algorithm is titled, Learning Semantic Textual Similarity from Conversations. The algorithm learns how to understand questions by studying responses as a way to understand what the questions really mean. Here’s how Google explains it:

“The intuition is that sentences are semantically similar if they have a similar distribution of responses. For example, “How old are you?” and “What is your age?” are both questions about age, which can be answered by similar responses such as “I am 20 years old”. In contrast, while “How are you?” and “How old are you?” contain almost identical words, they have very different meanings and lead to different responses.”

This algorithm is trained in conversational nuances using Reddit and other sources in order to be able to understand from actual conversations what questions mean.

It’s easier to understand similarities between long questions. But it becomes harder for short questions. The research claims to be able to train a machine to understand differences between short questions.

Here is what the researchers concluded:

How Could This Algorithm Be Used?

Nuance: An earlier research paper, Measuring the Sentence Level Similarity (not published by Google) on a related topic provides a description of how a similar algorithm could be applied in the real world:

“Sentence similarity measures are becoming increasingly more important in text-related research and application areas such as text mining, information extraction, automatic question-answering…”

Google’s research paper is silent as to how this algorithm could be used. However Google’s AI Blog post about these two algorithms, Advances in Semantic Textual Similarity noted that these algorithms allow them to use as few as 100 labeled examples to build useful text classifiers. That means that they can understand more with just a minimum of data instead of the millions and billions they’ve previously worked with.

Bill Slawski Comments

Nuance: I asked Google patent expert, Bill Slawski of GoFishDigital, about this algorithm and here is what he said:

I wrote about a Continuation patent from Google in the post, Google’s Related Questions Patent or ‘People Also Ask’ Questions. What was interesting about this updated patent was that it introduced the idea of a “question graph” for questions that Google might collect answers to.

Google told us that they would be creating datastores of natural language questions and answer in at least one patent: Natural Language Search Results for Intent Queries. By approaching identification of questions differently (with potentially some redundancy), it adds the ability to create a larger and potentially better question graph.

Why These Algorithms are Important

Google has a stated goal of creating an AI that is similar to the Star Trek computer. The Star Trek computer is the ideal that Google is reaching for. This has been a goal since at least 2013, probably earlier. Slate published an article in 2013, subtitled, Google has a single towering obsession: It wants to build the Star Trek computer. Here’s a key quote from that article:

“A few weeks ago, I was chatting with Tamar Yehoshua, director of product management on Google’s search. “Is there a roadmap for how search will look a few years from now?” I asked her. “Our vision is the Star Trek computer,” she shot back with a smile. “You can talk to it—it understands you, and it can have a conversation with you.””

The connection between the Star Trek computer and Google voice assistant are so close, that the Google voice project was initially named after the actress who played the voice of the Star Trek computer.

In the Star Trek make believe world the actors speak the trigger word, “Computer” and the computer listens and provides answers. In the real world we speak the trigger words “Ok Google” and Google’s voice assistant answers.

These algorithms can be seen as a contribution to Google’s goal of becoming like the computer in Star Trek. But there are also many other uses for these technologies beyond the question answering capabilities.

How the Star Trek Paradigm Resembles Google Voice Assistant

In the Star Trek world, interfacing with a computer is strictly a matter of speaking out a trigger word then asking a question. A typical scenario plays out like this:

In the Star Trek World:

This is Admiral James T. Kirk requesting security access.

Computer. Destruct Sequence One, code one, one-A.

COMPUTER VOICE: Destruct Sequence is activated.

In the Real World:

What is a good restaurant to eat in Madrid at an affordable price?

Google Voice Asssistant: The best cheap eats in Madrid are…

Is Google Alone in the AI First Paradigm?

No. It’s an open secret among technology companies that there is a race to becoming AI first. Qi Li, the COO of Baidu referenced the race towards becoming AI first in his statement announcing that he’s stepping down:

“”I’m honored to have participated in Baidu’s transition into an AI-first company,” Lu said. Microsoft and Google, among others, have also sought to reshape themselves around AI.”

Reshaping search in an Assistant Paradigm has been a longstanding goal at Google and it’s not alone. Even Zillow has been reported to be transitioning from Search to the AI Assistant Paradigm. A recent article in Mashable quoted Zilllow on AI:

“Currently, the site is undergoing what Wacksman describes as “the evolution from a search box to an assistant.” The ideas is to transform Zillow from a simple real-estate search engine to a tool that understands you.”

Takeaway: How Site Publishers Fit into the AI Assistant Paradigm

These technologies are reported to be in the beginning stages.

According to Google:

“We believe that what we’re showing here is just the beginning, and that there remain important research problems to be addressed, such as extending the techniques to more languages…”

Google also noted that these technologies fall short of understanding text at the paragraph and document level. Those are the next challenges on their way to transitioning to an AI Assistant.

It’s important to keep up on these developments because at some point there will be more indications of where publishers and merchants fit into this new world. This means paying attention to new developments in chúng tôi Structured Data requirements as outlined by Google in their developers pages.

Read Google’s announcement on their AI Blog, Advances in Semantic Textual Similarity

Read more about the voice search here.

Images by Shutterstock, Modified by Author

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Google Music Ai – Musiclm Explained

Last Updated on June 23, 2023

After the worldwide success of text-to-image ai generated visual artworks, the next logical step for artificial intelligence software was to venture into the world of music. Google announced the creation of its very own music generating ai MusicLM earlier this year as part of its Test Kitchen initiative. It has already caught the spotlight better than Jukebox music AI created by OpenAi, who also bought us the infamous ChatGPT and DALL-E.

If you’re wondering what exactly this new music creating machine is, everything you need to know about Google’s MusicLM is explained right here.

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MusicLM – what exactly is Google’s Music AI?

At its core, MusicLM is a prompt-based music creation tool driven by natural language text prompts.

What this means is that users will be able to type up written descriptions of what they want their music to sound like, using descriptions of genre, mood, and instrument, and MusicLM will generate this for them. It can also respond to a variety of detailed stipulations such as adjusting the level of experience of the musician that its’s emulating, and can even create music focused on a given context or situation like working out, or studying.

MusicLM will generate two clips of audio in response to any given prompt, which the user is then invited to select which they prefer in order to help train the AI and better its generative abilities.

By casting the process of conditional music making as a “hierarchical sequence-to-sequence modeling task”, Google has enabled MusicLM to create high-fidelity music over which users have creative freedom no matter their level of musicianship.

Google has showcased hours of music tracks that MusicLM created on detailed text prompts by Google researchers in short clips to demonstrate the impressive and vast capabilities of this innovative software.

How can I use MusicLM?

While Google has released Music LM to the public, you will be required to join a waitlist on the Google Test Kitchen website. We do not have official waiting times for how long it may take.

It also should be noted that this initial release is actually still intended to help train the AI model, and that it is relying on the use of feedback and application use data to further refine it.

Whether you are after a distorted guitar riff or calming violin melody, the world will soon be your oyster with MusicLM. This is also not Google’s first AI software release this year – Google Bard AI chatbot also released a few months earlier. If you’re interested in the developing sphere of artificial intelligence, you could check you some of the best AI tools of 2023.

Final Thoughts

The use of AI in music composition has clearly started to make its mark on the artificial intelligence industry, and this Google Arts AI model will certainly be one to keep an eye out for in future months.

However, as the conflict between generative AI and copyright law only continues to grow, it is clear that we need to continue to understand further how AI should be treated in the music industry, especially given how much they are improving at song generation.

Top 15 Ai Projects Powering Google Products In 2023

We already covered how AI is integral to Alphabet. We had left out Google. As AI is starting to power all Google products, Google deserves its own focus.

We are now witnessing a new shift in computing: the move from a mobile-first to an AI-first world.

Sundar Pichai @ blog.google

Current products

From smartphone assistants to image recognition and translation, a myriad of AI functionality hides within google apps that you daily use. We mapped this functionality leveraging Smart Faktory’s Google strategy framework.

Google the search engine is powered by AI: According to Wired’s Cade Metz; Google’s search engine was always driven by algorithms that automatically generate a response to each query. But these algorithms amounted to a set of definite rules. Google engineers could readily change and refine these rules. And unlike neural nets, these algorithms didn’t learn on their own. But now, Google has incorporated deep learning into its search engine. And with its head of AI taking over search, the company seems to believe this is the way forward.

Google Maps’ Driving Mode estimates where you are headed and helps you navigate without any commands.

Youtube Safe Content uses machine learning techniques to ensure that brands are not displayed next to offensive content.

Google Photos suggesting which photos you should share with friends.

Gmail Smart Reply suggesting replies that match your style and the email you received.

Google Drive Smart Scheduling suggests meeting schedules based on user’s existing schedule and habits.

Google Calendar Quick Access feature predicts which files will be used improving performance and user experience.

Nest Cam Outdoor leveraged machine learning to set up an automated outdoor security camera.

Google Translate uses an artificial neural network called Google Neural Machine Translation (GNMT) to increase fluency and accuracy of translations.

Google Chrome uses AI to

present short and highly related parts of a video while searching for something in Google Search.

analyze the images on a website and plays an audio description or the alt text(when available) for people who are blind or have low vision.

Google News uses AI to understand the people, places and things involved in a story as it evolves, organize them based on how they relate to one another as explained in Google Blog.

Google Assistant is a voice assistant for smart phones or wearables that can search online your flight status or the weather when you get there.  Touch and hold the Home button and find your Google Photos, access your music playlists and more. Both Siri and Google Assistant do a decent enough job of finding restaurants, bars, and other kind of businesses nearby, but Google’s app came out on top in our tests, not just on the places it returned, but on the interface: results are presented in a simple carousel and you can quickly jump to a Google Maps view. Also Google Assistant remembers what you’ve already said, speaks in foreign languages. Like Apple’s Siri, it is much more than an assistant, despite the name: it will read you poetry, tell you a joke, or play a game with you.

Google Home: You will be able to get hands-free help from your Assistant embedded in Google Home. Say “Ok Google” to get the morning news or manage your schedule.

Waymo an autonomous driving technology company became a subsidiary of Alphabet in 2024. Though Waymo announced in 2023 that they would be making self-driving cars available by 2023. We still don’t see them hitting the road as of 2023 due to regulatory concerns and complexity of self-driving.

For fun and experimentation you can visit AI experiments for videos on AI experiments.

Discountinued products

Messaging App Allo and its Smart Reply functionality was launched with significant functionality but shut down in 2023: Google joined the messaging app battle with Allo. As a messaging app, Allo lets you express yourself with stickers, doodles, HUGE emojis & text. Furthermore it brings you the Google Assistant without leaving the conversation. The AI algorithm used to learn your style and provide more suggestions, which makes your message more ‘you’. You could get creative with the photos you send by doodling on them or adding text or draw a smiley face, turn your friends into memes, and mix in some color.

Adding a new feature turning a selfie into an emoji, Google says that the algorithms powering the new tool are powerful, machine-learning technologies that could generate 563 quadrillion different faces. The search giant is using neural networks to create custom emojis.

Now that you know what Google is up to in AI, you can explore the rest of the AI universe. You can check out

You can also our list of AI tools and services:

And If you have a business problem that is not addressed here:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Microsoft Bing Ai Chat Open To All; Announces Many New Features

Microsoft’s Bing AI Chat has consistently been one of the best ChatGPT alternatives people have been using. Armed with OpenAI’s new GPT-4 language model, the AI search engine-cum-chatbot can do a lot in little time. However, in an attempt to make things even better, Microsoft has announced a slew of changes that bolster the bot with even more functionality. Keep on reading as we discuss all the major changes.

Bing AI Chat Goes into Open Preview

While already packed with major power, one of the downsides of Bing Chat was that it was locked behind a waitlist. However, that has now changed as Microsoft Bing AI has been moved from Limited to Open Preview. This means that everyone can now access Bing AI Chat without any boundaries. However, do note that you will still need the Microsoft Edge browser to access Bing’s Chat mode. But we have already shared a workaround to use Bing AI Chat in any browser.

So those without access to the waitlist can now start using Bing AI without issues. All you need to do is create a Microsoft account and get started.

Bing Actions; ChatGPT Plugins Come to Bing

If you read about ChatGPT getting plugins, then you already know about the power they can bring. These plugins can add more power to the bot by bringing in information from other sources. Well, Microsoft has caught on to the former’s success and is now announcing future support for third-party plugins (called Bing Actions) in the chat experience. This opens up a lot of possibilities as developers will now be able to build feature-centric plugins that will intelligently power Bing.

Bing AI Gets Major Chat Features

Besides the announcements above, chats themselves are also getting a bunch of new features poised to make the user experience easier.

For starters, Bing AI chats will soon deliver more visual and richer answers with the help of images and infographics like charts. The answers are also getting updated formatting to help users find information even quicker. Furthermore, Microsoft is soon bringing GPT-4’s multimodal capabilities to Bing AI. Users will soon be able to upload images and search the web for related content easily.

Microsoft has also listened to users by bringing some much-requested chat features. Starting soon, Bing AI will get a chat history, where users can return to previous conversations easily. As with ChatGPT, these chats will be marked by dates and easily sortable. Users will also soon be able to access chats even on different tabs using Persistent Chats through Edge.

For those looking to share their conversations like ChatGPT, Microsoft is soon adding the ability to export and share Bing chats. Users can easily export these conversations in proper formatting so they can easily transfer to companion software like Microsoft Word.

It is clear that Microsoft is arming Bing AI Chat with some serious virtual weapons to go up against any competitors. With Bing AI in tow with the best AI image generators, users can consider themselves a one person army. However, it will be fun to see what new developments come next.

Google Disbands Ai Committee Before First Meeting

Last week, Google announced that it had formed an AI Ethics committee to assist with the direction of its intelligent technology. This week came an update that few expected – the panel has already been dissolved.

Google has claimed that the committee “can’t function” as it wanted. Although, many believe that the reason for the sudden collapse of the panel is due to the negative reaction from Google staff and the public to one of its key members.

Google has claimed that it will continue to research ethics in AI, though through alternative means.

What Has the AI Ethics Committee Been Disbanded?

Shortly after Google’s announcement of its creation, the Advanced Technology External Advisory Council (ATEAC) came in for criticism. The backlash came from all fronts – the public and staff at Google – for some of the representatives that the company had chosen.

The focus of much of this disagreement was on Kay Coles James, the president of the Heritage Foundation – a conservative think tank. Questions were raised about her suitability as a representative on the panel, with a petition started by Google employees that she be removed from the panel due to her “anti-trans, anti-LGBTQ and anti-immigrant” views. The petition was signed by more than 2,500 staff, before being opened up to the public.

Shortly after this, another member of the panel, Alessandro Acquisti stepped down from council, stating on Twitter that “While I’m devoted to research grappling with key ethical issues and fairness, rights and inclusion in AI, I don’t believe this is the right forum for me to engage in this important work’.

Another member, Joanna Bryson, Associate Professor in the Department of Computer Science at the University of Bath, was questioned about her involvement with the group based on Kay Coles James’ link on Twitter, and replied “Believe it or not, I know worse about one of the other people“. Bryson stated that she had questioned Google on its right-leaning associations in the past, and said that the company had replied that “it need[ed] diversity in order to be convincing to society broadly, e.g. the GOP“.

In a statement given to Vox, a Google spokesperson confirmed that ATEAC had been dissolved, stating “ATEAC can’t function as we wanted”. However, it did not pinpoint the exact reason why, nor make any references to Kay Cole James or the Google staff petition.

What Were the Goals of the Ethics Committee?

As we reported last week, ATEAC has been tasked with shaping Google’s AI policies and resolving complex ethical decision, with facial recognition and machine learning among the topics to be discussed.

Their work was to complement Google’s “AI Principles”, a set of rules that the company established last year as it forged ahead in its AI research. Some have stated that these were a direct result of the company’s decision to not renew its contract with the Pentagon to supply AI drones. Its prior involvement with the military caused ire among Google staff, who felt that it went against the company values.

The seven principles include creating AI that benefits society, avoiding creating unfair bias, being accountable, and being built and tested for safety.

Has Google Now Abandoned AI Ethics?

In its short statement on the disbandment of ATEAC, Google stated that it was “going back to the drawing board”, and that it would continue to highlight the issues raised by AI. But, Google has said it would “find different ways of getting outside opinions on these topics”.

What form the next iteration of Google’s AI ethics plans takes remains to be seen. But, its statement suggests that it won’t be another panel. The problem that the company faces is that if it truly is looking for a diverse group of policy makers from both the right and the left, it will always struggle to have them see eye to eye. While Google may be able to include both sides in its debates, it can’t appease them both, too.

Skeptics had claimed that the ATEAC was limited in its ambitions, and that meeting four times a year wouldn’t allow for much progress on such a cutting edge issue that will only continue to become more prevalent. Whatever its next move, Google is sure to tread carefully.

Ai In Analytics: How Ai Is Shaping Analytics In 2023 In 4 Ways

I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, and citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive, and prescriptive analytics.

In that regard, in this article, we plan to explain how exactly AI is transforming how analytics is done.

How is AI contributing to analytics capabilities?

More efficient thanks to automation,

More accessible thanks to improved UI, with Natural Language Processing enabling analytics tools to understand natural language queries,

And more powerful, since previously-difficult-to-analyze data, such as text and videos, are now easily analyzable.

Analytics is getting automated

Analytics is time consuming and analytics talent is expensive. The war for AI talent is well documented and impacts the cost and availability of analytics talent. Data scientist who compromise a significant share of modern analytics work force are also part of the AI workforce.

Therefore automating analytics tasks has significant potential value for firms.

Analysis (i.e. discovering insights and acting on them) is getting automated

AI systems are able to analyze data autonomously. Based on the results of analysis, they can take automated actions or highlight insights to employees who can decide the best course of action.

Setting up auto notifications based on simple rules has been possible since the early days of computing. Now companies can set up complex, machine learning based triggers to identify insights or automate actions. For example, a machine learning system that detects intruders to physical secure locations based on video feeds can take actions (e.g. by informing the intruder) or highlight the event to the human personnel.

Report preparation is getting automated, making analytics more accessible

Analytics are only as useful as long as it is accessible. Natural language generation (NLG) enables automated report preparation.

Analytics is becoming more accessible

We mentioned the increasing cost of analytics talent due to the increased demand for data science talent. Data scientists are expensive as they are PhDs or graduates of computer science and other quantitative fields who have an understanding of statistics and computation. This enables them to build complex models including machine learning models which can extract insights from data.

What if you did not need data scientists to extract insights from data? Users can use natural language to easily and intuitively find answers. This is supported through natural language (NL) query (also called natural language interaction – NLI or natural language user interaction – NLUI). For example, Thoughtspot, which enables companies to run complex analytics queries via natural language, raised ~250m on a ~2 billion valuation in 2023.

Scope of analytics is increasing thanks to AI Unstructured data is becoming analyzable thanks to AI

Excel and other legacy analytics tools are not effective at dealing with unstructured data such as text, audio and images. Advances in AI greatly expand the scope of analytics when compared to the days when excel was the primary analytics tool. Some ways that AI is becoming integrated in analytics includes these areas:

Natural language processing (NLP) enables analysis of text

Transcription enables speech analytics

Computer vision enables image and video analytics

Semi-structured data is becoming analyzable thanks to AI

A significant share of company data is locked up in semi-structured documents such as invoices, receipts, order forms etc. Deep learning based data extraction solutions enable companies to extract entities from their semi-structured data and use them to understand their business in more detail.

New techniques enable analysis of anonymized personally identifiable data, expanding scope of analytics

Anonymization via synthetic data is a rather old technology. However with the increasing demand for analytics and increasing protection of personal data, demand for anonymized data has increased. Numerous synthetic data vendors are enabling companies to create synthetic (machine-generated, anonymized but following the same distributions as the underlying personally identifiable data) copies of their customers so they can run detailed simulations and improve their offering.

Analysis is becoming more powerful thanks to AI

While simple regressions guided business decision making for hundreds of years, businesses now rely on machine learning. Machine learning is the use of statistical techniques to enable computers to identify and learn the patterns in the given data, rather than being programmed explicitly for a certain function.

Some analytics techniques that can be enhanced with AI and machine learning include:

Prediction: Using short and long term variability in data to enhance forecasting efforts.

Pattern recognition: Understanding normal trends in order to spot anomalies, as is often the case in fraud detection.

Classification algorithms: Grouping and organizing of data, includes clustering.

Which industries rely on AI in analytics?

All industries can benefit from AI-powered analytics. We had a deep-dive into:

Some tools that can make the analytics process easier include:

NameStatusNumber of Employees Apache PredictionIOPrivate1,001-5,000 AyasdiPrivate51-200 Einstein Analytics by SalesforcePublic10,000+ IBM CognosPublic10,000+ Infosys niaPublic10,001+ IperceptionsPrivate51-100 Microsoft Power BIPublic10,000+ MicroStrategy IncPublic1,001-5,000 Oracle Analytics ClousPublic10,000+ PurePredictivePrivate21-50 Qlik SensePrivate1,001-5,000 SAP Analytics CloudPublic10,000+ Sisense IncPrivate501-1,000 StatisticaPrivate10,001+ TableauPublic1,001-5,000 TelliusPrivate11-50 ThoughtSpotPrivate201-500 TIBCO Software IncPrivate1,001-5,000

For more on analytics

If you are interested in learning more on analytics, read:

Finally, if you believe your business would benefit from leveraging an analytics solution, we have data-driven lists of vendors on our analytics hub.

We will help you choose the best one suited to your needs:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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