### Next-generation Bing

In the UK, Google dominates the search engine market with a whopping 93% share, while Bing lags behind with only 4%. When we look at desktop devices, where Windows is the dominant platform, Bing enjoys a relatively stronger presence, with 13% of the market share, but still Google is dominating all brands with an 83% presence. 

**So how does a brand significantly grow its presence?**

Microsoft has made a significant announcement regarding a major update to its search engine, Bing. The update entails the integration of a new and advanced OpenAI large language model, which has been customised specifically for search purposes. The new model is based on the latest advancements and key learnings from OpenAI's ChatGPT and GPT-3.5 models, making it even more powerful, faster, and more accurate. The new language model provides Bing users with an unprecedented level of search capabilities, delivering results that are both relevant and swift. This latest update is poised to shake up the search landscape and set a new standard for search engines everywhere.

Microsoft put it plainly:
>"There are 10 billion search queries a day, but we estimate half of them go unanswered. That’s because people are using search to do things it wasn’t originally designed to do. It’s great for finding a website, but for more complex questions or tasks too often it falls short."

### Bing Chat is outsmarting ChatGPT

Surely, you have heard of ChatGPT, the language model developed by OpenAI, which is dominating social media channels. It operates based solely on statistical associations derived from its training set of word co-occurrences. This means that it lacks real-world knowledge and is unable to answer questions about current events, math, logic, general knowledge, and more. Instead, when faced with such questions, it generates fictional responses that may sound plausible but lack factual accuracy.

### How is Bing leveling up?

In comparison, the large language model utilized by Microsoft for Bing operates differently. The model, named "Prometheus", automatically uses Bing in order to find possible answers to the user query, and then summarises key findings in its chat response.
The name choice of the model seems apt: Prometheus, after all, was the Greek mythological figure who stole fire from the gods. This theft was significant, as the fire represented knowledge that allowed humans to control their own fate and destiny independent of the will of the gods. Similarly, Prometheus, the language model utilized by Bing, is designed to bring knowledge and information to users in a more accessible and user-friendly way. The model's reliance on Bing search results means that it is better equipped to provide up-to-date and accurate information, allowing users to take control of their own searches and find the information they need quickly and efficiently. 

![AdobeStock_562590366.jpeg](https://pome-backend.fra1.digitaloceanspaces.com/caf6e77b558dc828b706676bd329bd49.jpeg)

The name "Prometheus" therefore represents the model's ability to empower users with knowledge and information, just as the mythical figure empowered humans with fire. In this sense, the name is a fitting symbol of the model's role in revolutionizing the search experience for Bing users.

To give users even more control and certainty about the factual accuracy of returned responses, Bing goes beyond simply providing aggregated responses to queries. Each response is accompanied by a list of sources, allowing users to easily verify and cross-reference information. This added transparency and accountability further enhance the reliability and accuracy of Bing's search results.

Practically, grounding chat responses of Prometheus in Bing search results makes it possible to provide more comprehensive and relevant answers to users' queries. For example, during a demonstration of Bing's capabilities, the model was asked to create an itinerary for a 5-day trip to Mexico City. The chatbot was able to provide a detailed itinerary, including a description of activities for each day of the trip, along with links to relevant sources for additional information.
This example showcases the potential for Bing to be a one-stop-shop for users' informational needs, providing not only answers to their questions, but also relevant links and resources for further exploration. This greatly enhances the user experience, as it saves time and effort by eliminating the need to conduct multiple searches or sort through irrelevant information.

![Screenshot 2023-02-15 at 16.54.15.png](https://pome-backend.fra1.digitaloceanspaces.com/8d40c5dfc4a3001decdadb99f784d05b.png)

Watch the video [here](https://www.youtube.com/watch?v=zhdwVdSFn54)

### Bings making a comeback!

OpenAI models will also be integrated into the next-generation Edge browser through the introduction of two cutting-edge AI-powered features, "Chat" and "Compose". These innovative tools will be integrated within Edge's sidebar, providing users with a more seamless and intuitive browsing experience.

The "Chat" feature allows users to easily summarise web pages or documents they're viewing and ask questions related to its contents. In short, the "Chat" function integrated within Edge provides a unique and exciting opportunity for users to converse with websites or PDF documents and retrieve specific information or additional data from the web through Bing, all without ever leaving the chat interface.

On the other hand, the "Compose" function serves as a writing assistant, helping users to generate text, whether it be for emails or social media posts, based on a few starting prompts. This AI-powered writing assistant takes the hassle out of crafting coherent and well-written communications. While AI copywriters are now commonplace, having them integrated in the browser, without the need to use dedicated apps or websites, is a user experience improvement.

All of which could look to be a promising start to winning back some of the market for Bing. 

![image 89.png](https://pome-backend.fra1.digitaloceanspaces.com/8b20e2d2f577d22a364359b9b32854e9.png)

### Could Bing fail?
With all of these features being available for free, one wonders how Microsoft is planning to recoup the costs it will incur on part of the users requiring vast amounts of compute?

**Generating false answers**
When Google presented Bard, a conversational bot which was positioned as the answer to Bing's Chat functionality, it cost the company $100 billion in market value. Why? Because during its first presentation, it gave a wrong answer. 

Errors like this are due to large language models not possessing any real world knowledge outside of the statistical associations between words, and any knowledge grounding being dependent on search results, which might also be factually wrong or be misinterpreted by the natural language processing algorithms of the model.

With Bing Chat already being available to early access users, both of these problems, namely inaccuracies in search results and failings of natural language processing can be seen. See, for example, the screenshot below:

![Screenshot 2023-02-15 at 16.58.16.png](https://pome-backend.fra1.digitaloceanspaces.com/d55033451c418d92392961cd40e75739.png)

When asked about why Bard failed, Bing Chat produces a wrong answer in turn. Rather than correctly stating that Bard gave wrong information, it confabulates a story about a question pertaining to the number of states in the European Union. Further, contrary to Bing's claim, Croatia did not leave the EU in 2022. In fact, it remains a member state. The references given for all of these false claims in the Chat Bing response are self-referential errors (Bing referring to itself), and misinterpreted text from two other websites. This is one example of how large language models can fail even basic tasks of knowledge parsing.

**AI gone rogue**

Large language models such as ChatGPT, Bing Chat and Bard produce text based on a sequence of inputs. Part of that input is the user query, and another much larger part of the input is hidden from the user, but appended to their prompt automatically. This appended prompt, usually as a prefix to the user query, are generally multiple pages long, has been carefully written by AI engineers, and attempts to both constrain and direct the output consequently generated by the system. In this way, engineers attempt to limit the degree of factual inaccuracies or possibly confusing or toxic comments made by the model. However, large language models are complicated and user input is unpredictable, so in many situations the output of the model will breach these attempts of constraints. See some examples below:

![Screenshot 2023-02-15 at 17.00.13.png](https://pome-backend.fra1.digitaloceanspaces.com/32628dc7bd1b04ce589375342e84ab1f.png)

Here Bing Chat (the white text bubbles on the left side) accuses the user of misconduct and refuses to share information about the user’s search request (the showing times of the most recent Avatar movie) until the user apologises for their behaviour.

![Screenshot 2023-02-15 at 10.47.04.png](https://pome-backend.fra1.digitaloceanspaces.com/2e7289129220f4be91f4393f07628552.png)

Lastly, in this example, as the user tries to understand whether the Chat is indeed sentient (it is not), the generated output text loses itself in a somewhat disturbing loop of the same words repeated over and over again, which might be distressing to some users, who do not understand that they are not conversing with an intelligent system.

### Summing Things Up

In conclusion, Microsoft's new update to Bing and Edge browser with the integration of Large Language Model support is a major development in the search engine landscape. With the ability to provide more accurate and up-to-date information and the introduction of new AI-enhanced features, this update has the potential to bring a significant influx of users to Bing and Edge. While Google currently dominates the market, the integration of advanced AI technology into Bing and Edge offers an exciting new alternative to users who are looking for a more personalized and informed online experience. The future of search engine technology will be worth keeping an eye on, as Microsoft continues to push the boundaries of what is possible with AI.


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Has Microsoft's Bing outsmarted ChatGTP? 

Generative AI for video creation is making strides with recent advancements in technology. A Google team has presented a model called Dreamix, which allows users to give an input video and a text prompt for video transformation or editing. 

The Google team has described the model as using a video diffusion model to combine low-resolution spatiotemporal information from the original video with newly synthesised high-resolution information that aligns with the guiding text-prompt. 

Alternatively, it also enables users to give input images and a text prompt to generate videos from scratch. For this, the model first transforms the image into a coarse video by perspective geometric projections of image replications, and then deploys a custom general video editor to animate it, based on associations the model has learned between certain geometric patterns and their respective movements.

Watch Dreamix in action [>here<](https://www.youtube.com/watch?v=xcvnHhfDSGM).

While Google has not yet announced plans to release their model, things are moving quickly in the AI world.

Runway ML, one of the two startups behind the Stable Diffusion model, which has powered almost all commercially successful AI image generation services, has, just one day after the Google team, announced their Gen-1 model, which looks to deliver the same concept, albeit at lesser visual quality compared to Dreamix.

Watch Gen-1 in action >[here](https://www.youtube.com/watch?v=fTqgWkHiN0k)<.

### Runway ML identifies 5 key applications:

- **Stylisation**: With this mode, users can transfer the style of any image or text prompt to every frame of their video, creating a consistent and visually appealing output.

- **Storyboard**: This mode takes mockups and transforms them into fully stylised and animated renders, bringing static designs to life.

- **Mask**: By using this mode, users can isolate subjects in their video and modify them with simple text prompts, allowing for more precise editing and customisation.

- **Render**: This mode turns untextured renders into realistic outputs by applying an input image or prompt, producing more lifelike and polished final results.

- **Customisation**: This mode allows users to fully unleash the potential of Gen-1 by customising the model for even higher fidelity results, enabling them to produce even more visually stunning content.


Runway ML has just opened up access to their new video creation model, so you can already get your hands on it. Even better, even though their tools have been used by both enterprise customers like CBS’s Late Show with Stephen Colbert or the Hollywood visual effects team behind Everything Everywhere All at Once, all the Runway ML tools are accessible for just $12 a month for individual creatives, and $28 for entire teams. This accessibility is the reason as to why Runway ML is one of the companies to watch in the generative AI space - and thanks to them you too will soon be able to create more visually stunning content at lower prices than ever before.

### Summing Things Up

In conclusion, the recent advancements in generative AI for video creation have opened up a world of possibilities for individuals and businesses alike.
With models like Dreamix from Google and Gen-1 from Runway ML, users can now create stunning videos with ease, by either transforming existing videos or generating them from scratch.

Whether you're looking to stylise your video, turn mockups into fully animated renders, isolate subjects in your video, turn untextured renders into realistic outputs, or unleash the full power of the AI by customising the model, these tools offer a wide range of options to meet your specific needs.

With affordable pricing and easy accessibility, there's never been a better time to dive into the world of generative AI and start creating visually stunning videos.

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Creating Stunning Videos with the Power of AI: The Latest Advances

In today's competitive market, understanding the customer journey is crucial for any business looking to increase revenue. The customer journey is the series of interactions and experiences that a customer goes through when interacting with a brand or business. Identifying key touchpoints in the customer journey is essential for understanding where customers may be experiencing pain points, and where opportunities for improvement exist. This article will provide a comprehensive guide on how to identify key touchpoints in the customer journey and analyse customer behaviour and feedback at each touchpoint.

### How to identify key touchpoints in the customer journey

The customer journey is the series of interactions and experiences that a customer goes through when interacting with a brand or business. Identifying key touchpoints in the customer journey is crucial for understanding where customers may be experiencing pain points, and where opportunities for improvement exist.

There are several ways to identify key touchpoints in the customer journey, including:

- Conducting customer interviews and surveys to gather feedback on their experiences. 

- Analysing website and social media analytics to understand where customers are spending the most time and engaging with the brand. 

- Identifying common points of contact with customers, such as phone calls, email, or in-person interactions. 

- Utilising heat mapping and click tracking tools to understand how customers are navigating through the website. 

- Analysing customer behaviour data such as purchase history and customer lifetime value. 

Once key touchpoints have been identified, it is important to analyse customer behaviour and feedback at each touchpoint in order to understand the customer experience and identify areas for improvement. This can be done by:

-  Using customer feedback surveys to gather qualitative data on customer experiences. 

-  Analysing customer behaviour data such as conversion rates, bounce rates, and customer lifetime value. 

-  Identifying patterns in customer complaints or areas where customers are dropping off in the journey. 

By understanding the customer journey and identifying key touchpoints, businesses can make informed decisions on where to focus their efforts in order to optimise the customer experience and increase revenue.

![image 98.png](https://pome-backend.fra1.digitaloceanspaces.com/f012c691f9f83ff196f4badf84fbfad9.png)

### Getting Practical

In Google Analytics, you can analyse customer behaviour and feedback at each touchpoint by using various tools such as the following:

- **User Flow Reports**: This report allows you to see how customers navigate through your website, including which pages they visit, how they interact with those pages, and where they drop off in the journey. You can use this information to identify where customers are struggling or getting stuck, and make adjustments to improve their experience.

- **Event Tracking**: You can track specific actions on your website, such as button clicks or form submissions, using event tracking. This allows you to see which touchpoints are driving the most engagement and conversions.

- **Site Search**: By tracking the terms that customers are searching for on your website, you can gain insight into their needs and pain points. This can help you identify areas where you need to improve the customer experience or provide more information.

- **Custom Segments**: Create segments of customers based on their behaviour and feedback, and analyse their interactions with your website. For example, create a segment of users who have searched for a specific product or service on your website, and see how they interact with it.

Lastly, you can use heatmap and session recording tools like Hotjar to track user clicks and movements on a website, providing insights into what customers are looking for, and where they are struggling.

By using these tools, you can gain a deeper understanding of customer behaviour and feedback at each touchpoint in the journey, and use that information to optimise the customer experience and increase revenue.

![image 99.png](https://pome-backend.fra1.digitaloceanspaces.com/115e8bf6ce472d252d360e4d0961744e.png)

### Losing Your Way with Journey Analysis

- While understanding the customer journey is crucial for improving the customer experience and increasing revenue, there are potential pitfalls that can arise if the process is not conducted carefully.

- Incomplete or inaccurate data: If the data used for customer journey analysis is incomplete or inaccurate, it can lead to incorrect conclusions and misguided efforts to improve the customer experience. Emoti's AI-driven approach ensures that the most accurate and comprehensive data is used for analysis, minimising the risk of drawing false conclusions.

- **Overlooking qualitative data**: Focusing solely on quantitative data can result in an incomplete understanding of the customer experience. Emoti's solution incorporates qualitative data, such as customer feedback and sentiment analysis, to provide a more holistic view of the customer journey and uncover hidden pain points and opportunities for improvement.

- **Failure to consider customer segmentation**: Not all customers are the same, and their journeys may differ significantly. Failing to account for different customer segments can lead to a one-size-fits-all approach that may not resonate with all customers. Emoti's advanced analytics capabilities automatically segment customers based on their behaviour, preferences, and needs, enabling businesses to tailor the customer experience for each segment and maximise satisfaction and revenue.

- **Limited adaptability**: Customer needs and market conditions change over time, and businesses must be able to adapt their strategies accordingly. Emoti's AI-driven approach continually analyses data and identifies emerging trends, ensuring that businesses can stay ahead of changing customer expectations and market dynamics

### Summing Things Up

In conclusion, identifying key touchpoints in the customer journey and analysing customer behaviour and feedback at each touchpoint is crucial for any business looking to increase revenue. By understanding the customer journey and identifying key touchpoints, businesses can make informed decisions on where to focus their efforts in order to optimise the customer experience and increase revenue. This can be done through various methods, such as conducting customer interviews and surveys, analysing website and social media analytics, and utilising tools such as Google Analytics, Typeform, and Hotjar.

However, it is essential to recognise the potential pitfalls associated with customer journey analysis, including incomplete or inaccurate data, overlooking qualitative data, neglecting customer segmentation, and limited adaptability. Emoti, an AI-driven solution, addresses these challenges by providing comprehensive and accurate data analysis, automatic customer segmentation, and ongoing adaptability to ensure businesses can continually optimise the customer experience and maximise revenue.

By leveraging innovative tools, methods, and solutions like Emoti, businesses can gain a deeper understanding of customer behaviour and feedback at each touchpoint in the journey. This information can be used to optimise the customer experience, address pain points, and capitalise on opportunities for improvement, ultimately leading to increased revenue and enhanced customer satisfaction.
By using these tools and methods, businesses can gain a deeper understanding of customer behaviour and feedback at each touchpoint in the journey, and use that information to optimise the customer experience and increase revenue.

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The Journey to Conversion: How to Optimise the Customer Experience for Increased Revenue

Businesses are constantly gathering data about their customers' online behaviour to offer more personalised services. But businesses haven’t considered that this increased personalisation can lead to a paradoxical decrease in customer adoption rates, known as the "personalisation paradox." 

A study by Aguirre and colleagues on a personalised advertising campaign on Facebook found sharp drops in click-through rates when customers realised their personal information had been collected without their consent.
So we are going to delve into this paradox and examine the interaction between information collection and its personalisation uses. We will explore how customers react to online personalised advertising, and the role of information collection, feelings of vulnerability, and trust-building strategies in determining personalisation effectiveness.

### The Personalisation Paradox

The personalisation paradox refers to the fact that while greater personalisation typically increases service relevance and customer adoption, it can also increase customers' sense of vulnerability and ultimately lower adoption rates. This phenomenon is detailed in a study on a personalised advertising campaign on Facebook by Aguirre and colleagues, which found that when customers realised their personal information had been collected without their consent, there was a sharp drop in click-through rates.

Personalisation and data collection techniques often leave customers feeling uncomfortable and vulnerable, particularly when the end result is them being confronted with personalised cues, such as targeted advertisements. This feeling of vulnerability can lead to a rejection of the personalised service, resulting in lower adoption rates, and can also leave customers feeling like they are unable to trust a brand.

To understand this paradox, it is important to examine the role of information collection and trust-building strategies in determining personalisation effectiveness. When businesses openly inform their customers that data collection has taken place and give them the option to opt-in or opt-out, customers tend to find the self-referential and relevant content of the personalised advertisements more useful, leading to increased behavioural intentions. On the other hand, when businesses collect information covertly, customers may reject the personalised content and exhibit lower behavioural intentions.

Furthermore, trust-building strategies can play a crucial role in offsetting the negative effects of covert data collection. When an advertisement appears in a credible context or incorporates information icons that signal trustworthiness, consumers are more likely to trust the source and not feel vulnerable.

In summary, the personalisation paradox highlights the importance of transparent information collection practices and the use of trust-building strategies to effectively personalise services and increase customer adoption rates. It is important for businesses to strike a balance between personalisation and customer privacy to provide a positive and beneficial experience for the customer.

![image 75.png](https://pome-backend.fra1.digitaloceanspaces.com/49ea37bfda8c71554e94a009e29a8e9e.png)

### Trust-building strategies

By applying the theory of psychological ownership, we can clarify the influence of an effective vulnerability experience: customers want to feel in control of their data and understand what parts of themselves are visible to the company. If they feel like too much is revealed without their awareness, they feel like their privacy is being infringed on unduly, resulting in them closing themselves off to messaging. This highlights the importance of addressing vulnerabilities and ensuring a consumer's right to information and voice in pertinent decisions to mitigate the negative effects of personalisation.

Businesses can implement to build trust with customers and offset this negative effect. The study suggests that transferring trust from another website or signaling trust with informational cues can help to alleviate customers' feelings of vulnerability.

![image 77.png](https://pome-backend.fra1.digitaloceanspaces.com/c5e2debcd3184d153bd67ee0603e0898.png)

### Case study: Amazon

Amazon gives users a clear way to view their browsing history, turn it on and off with a simple switch, and even delete individual items from view.

![image 82.png](https://pome-backend.fra1.digitaloceanspaces.com/a29391f780eb292e8aef819655d1b992.png)

Further, for each item in your browsing history, Amazon allows you to toggle a switch indicating whether this item should be used to inform product recommendations.

![image 83.png](https://pome-backend.fra1.digitaloceanspaces.com/d408b9a669e1bb06db5f58334bce4f7c.png)

Once a recommendation has been made, the user can manually remove the recommendation, to further fine-tune future ones made by the system.

![image 84.png](https://pome-backend.fra1.digitaloceanspaces.com/b3b7c496fd4be0061e65b27c5251e364.png)

Lastly, recommendations made as such are labelled accordingly, making it easy to understand why the items are being displayed to you.

![image 85.png](https://pome-backend.fra1.digitaloceanspaces.com/37d681a7c9bb4a95fd464cbfd07129fb.png)

This approach checks all boxes for how to do personalisation correctly. Allowing users to have full insight and control over how their data is used to shape recommendations and making it clear what elements of the site are impacted by the collected data gives the users ownership over it, which allows them to not feel vulnerable. Through this the personalisation becomes a boon rather than a bane.

### Summing Things Up

In conclusion, this article has highlighted the importance of understanding the personalisation paradox in order to create effective personalised marketing strategies that balance service relevance and customer adoption while also addressing customers' sense of vulnerability. 

A review of the literature has found that customers' awareness and ad hoc consent have central influences on personalisation effectiveness. It was also found that rejections of these personalised services often stem from feelings of vulnerability, which arise when consumers confront a personalised cue. This effective vulnerability experience was found to be mediated by ensuring a consumer's right to information and voice in pertinent decisions. 

Additionally, studies found that trust-building strategies such as personalised data appearing in a credible context or incorporating information icons that signal trustworthiness, can offset the negative effects of covert data collection. 

These findings emphasise the importance of considering the role of information collection and trust-building strategies in personalisation efforts and how it can offset feelings of vulnerability.

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The Personalisation Paradox: The importance of trust

Music is a form of artistic expression that has been enjoyed by people for centuries. It has the power to evoke emotions, bring people together, and create memories that last a lifetime. With advancing technological development, music has become more accessible than ever before. People can now listen to their favourite songs anytime, anywhere, and on a variety of devices. However, creating new and original music still remains a challenging task for even the most talented musicians.

To address this issue, Google has developed MusicLM, a state-of-the-art machine learning model that generates music from simple text descriptions or from someone simply humming a melody. This model has been trained on a large dataset of music-text pairs with rich text descriptions of each piece created by human experts. The MusicLM model can generate music in various styles and genres, ranging from classical to pop, and can even compose entire pieces of music from scratch.

In this article, we will briefly explore the technology behind MusicLM, its capabilities, and its impact. Whether you are a music lover or a technology enthusiast, this article will provide you with a comprehensive understanding of MusicLM and its place in the world of music.

![Frame 26.png](https://pome-backend.fra1.digitaloceanspaces.com/7e86a4e52479986214d2548f9f9fbe18.png)


### The Development of MusicLM

MusicLM is a cutting-edge artificial intelligence technology developed by Google, which, aside from having developed the dominant web search engine, is also one of the world's leading AI research companies. The development of MusicLM has been a major milestone in the field of AI, as it represents a significant step forward in the ability of AI systems to understand and generate musical content.

Before the development of MusicLM, AI systems had limited ability to understand and generate music, as they lacked the capacity to process the complex and nuanced relationships between musical elements such as rhythm, melody, harmony, and timbre. However, with the development of MusicLM, this has changed, as the technology is capable of processing and generating musical content with unprecedented accuracy and creativity.

MusicLM's development team used advanced machine learning algorithms and deep neural networks to train MusicLM on a massive dataset of musical content, allowing the technology to learn and understand the patterns and relationships between different musical elements and rich text descriptions typed up by human experts in painstaking manual labour.

The result of this effort is cutting-edge AI technology that has the ability to generate high-quality musical content in a wide range of styles and genres, making it a powerful tool for musicians, composers, and music producers.


### Features of MusicLM

First introduced in the paper "MusicLM: Generating High-Fidelity Music from Text Descriptions", MusicLM is special because it can make high-quality music that stays consistent and true to a written description of what the music should sound like.

Specifically, MusicLM casts the process of generating music as a hierarchical sequence-to-sequence modelling task and generates music at 24 kHz. This means that the model takes a text description of a piece of music, such as "a calming violin melody backed by a distorted guitar riff", and generates an audio file that matches the description.

Think of a written description as a recipe for cooking. If you follow the recipe step by step, you should end up with a finished product that tastes the way it was meant to. MusicLM works in a similar way - it takes a written description of what music should sound like and creates a music track that follows that description.

See [this example](https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2FEmoti%2FgBZR2ejLlb.wav?alt=media&token=33aa85ad-cd8b-4fca-9b6b-90dbe13a047e) based on the following prompt: "The main soundtrack of an arcade game. It is fast-paced and upbeat, with a catchy electric guitar riff. The music is repetitive and easy to remember, but with unexpected sounds, like cymbal crashes or drum rolls."

What sets MusicLM apart from previous systems is its ability to be conditioned on both text and a melody input. For example, a user can whistle or hum a melody and the model will transform it into the style described in a text caption. This opens up new possibilities for creative expression, as users can input their own melodies and have them transformed into various musical styles.

Just like you can follow a recipe to make a cake, MusicLM takes your humming or whistling and turns it into a complete piece of music. See a video of this [here](https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2FEmoti%2FPME7AZ-eqS.mp4?alt=media&token=47d037d6-cc6e-4fc5-a7b3-a0cac0ea9f0a)!

MusicLM has the ability to generate music in a variety of genres including classical, rock, pop, hip hop, and more. It is trained on a diverse range of music and is able to produce music in different styles and instruments. Whether you want to create a symphony or a rap beat, MusicLM has got you covered.

It can also be given specific criteria, such as a particular genre, mood, or tempo, to generate music that meets those requirements. For example, if you want a sad, slow song in the classical style, MusicLM can generate music that matches those criteria. This ability to tailor music to specific requirements makes MusicLM an incredibly powerful tool for music production.

As the turnaround time from initial idea to first generated song can now be only a few minutes, this makes MusicLM an ideal tool for film and TV composers, who can use it to quickly generate musical ideas and sketches.

Of course, as with any new technology, there are important ethical considerations to take into account when using MusicLM. These may include issues related to ownership and control of music generated by the AI, as well as the potential for the technology to be used to manipulate or deceive listeners. Still, it is a big step forward in the field of music creation. To help other researchers and creatives, Google also released a dataset of 5,500 written music descriptions for others to use and build upon, to further the development of even better systems.


### Summing Things Up

Overall, MusicLM is a tool that makes creating music easier and more accessible. With its ability to follow written music descriptions and turn humming into music, MusicLM opens up new doors for creative expression and exploration. Its various features and applications have the potential to revolutionise the music industry and bring music creation to the masses. While there are still some ethical considerations to be addressed, the future of MusicLM is bright and full of possibilities. As this technology continues to advance, we can expect even more exciting and innovative applications that will change the way we think about and create music.


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Creating Music Just Got Easier with MusicLM: The Revolutionary Music Generation Tool

In today's competitive business landscape, targeting the right customers is crucial for success. Segmentation, the process of dividing a market into smaller groups of customers with similar needs or characteristics, is one of the most effective ways to identify and target your ideal customer. This article will explore the art of segmentation and its importance in creating effective marketing strategies through using machine learning techniques on data from Google Analytics (GA).

### The Importance of Segmentation

Segmentation is a powerful tool for businesses of all sizes. It allows you to tailor your marketing efforts to specific groups of customers, increasing the relevance and effectiveness of your message. By identifying and targeting your ideal customer, you can increase your return on investment and grow your business.

Additionally, segmentation can help businesses understand the needs and preferences of their customers, which can lead to product and service improvements. By focusing on specific segments, businesses can identify new opportunities for growth and expansion.

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### The Basics of Segmentation

There are several ways to segment your market, including demographic, geographic, psychographic, and behavioural.

**Demographic segmentation** involves dividing the market based on characteristics such as age, gender, income, and education.

**Geographic segmentation** involves dividing the market based on location, such as country, region, or city.

**Psychographic segmentation** involves dividing the market based on lifestyle, values, and personality.

**Behavioural segmentation** involves dividing the market based on customer behaviour, such as purchasing habits and brand loyalty.

By using a combination of these methods, you can create a detailed picture of your ideal customer and tailor your marketing efforts to reach them.

### How to Segment Data with Google Analytics

When it comes to identifying and targeting your ideal customer, advanced techniques like using AI and machine learning can be extremely helpful. One specific method is using k-means clustering, which is a type of unsupervised learning algorithm that groups similar data points together.

Let's say you're a clothing retailer and you want to segment your customer base by their purchasing behaviour. You can use Google Analytics to gather data on things like the products they've bought, how much they've spent, and how often they make a purchase.

Then, you can use k-means clustering to group these customers into different segments based on their similarities. By identifying these segments, you can tailor your marketing efforts and product offerings to better target and appeal to each specific group. To use it, you first need to decide how many groups you want to create. This number is called k, and it determines the number of segments you'll be able to identify. Once you've decided on k, the algorithm starts by picking k random points as the centre of each group. Then, it looks at all the other data points and assigns each one to the group whose centre is closest. After that, the algorithm updates the centre of each group to be the average of all the data points in that group. This process is repeated until the groups stop changing or a maximum number of iterations is reached.

When the algorithm is done, you'll have k groups, each one representing a different segment of customers. For example, you might find that one group is made up of older, high-income customers who buy expensive products, while another group is made up of younger, lower-income customers who are more price-sensitive.

For example, assume you have exported GA data on age, location and user interests as a CSV, you can then use a k-means algorithm as follows:

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Here are some of the most important Google Analytics (GA) metrics for customer segmentation:

- **Demographic**: Age and gender

- **Geographic**: City, region, and country

- **Behavioural**: Device used/operating system, page views, engagement rate, time on site, conversions, transactions and revenue, goal completion and goal conversion rate.

- **Psychographic**: search terms, source, medium, and campaign leading to user acquisition, in-market segments and affinity categories.

These metrics can be used in combination to create a detailed picture of your customer segments. By analysing the data provided by GA, you can identify patterns and trends in the behaviour of your customers, which can help you create more targeted and effective marketing campaigns.

### The Perils of Segmentation

While segmentation offers numerous benefits to businesses, there are potential pitfalls that can arise if it's not done correctly.

- **Over-segmentation**: Dividing the market into too many small segments can lead to diluted marketing efforts and increased costs. This occurs when businesses try to cater to every possible customer group, resulting in inefficient use of resources and ineffective marketing campaigns. Emoti's AI-driven approach analyses data to identify the most relevant and impactful segments, ensuring optimal allocation of resources and focused marketing efforts.

- **Inaccurate segmentation**: If segmentation is based on incorrect or outdated data, it can lead to targeting the wrong customer groups. This could result in wasted marketing budget and lost opportunities for growth. Emoti's cutting-edge artificial intelligence techniques ensure that the latest and most accurate data is used to create audience segments, minimising the risk of targeting the wrong customers.

- **Ignoring evolving customer preferences**: Customer preferences and needs can change over time, and businesses must adapt to stay relevant. If segmentation is not updated regularly, marketing efforts may no longer resonate with the target audience. Emoti's AI-driven approach continuously analyses data, identifying emerging trends and shifts in customer preferences to keep segmentation up-to-date and relevant.

- **Overlooking cross-segment opportunities**: Focusing too heavily on individual segments can cause businesses to miss opportunities that span multiple segments. Emoti's advanced analytics capabilities allow businesses to identify potential synergies between segments and capitalise on these opportunities, leading to increased revenue and growth.

This is where Emoti comes to the rescue. By cleverly automating the segmentation process, Emoti eliminates the risks associated with manual segmentation and provides businesses with valuable insights and recommendations for reaching their target audience effectively.

- **AI-driven audience insights**: Emoti's powerful algorithms analyse data from various sources, including Google Analytics, to create accurate and detailed customer segments. This enables businesses to gain a deeper understanding of their customers and their preferences, which can be used to inform marketing strategies and product development.

- **Clear recommendations**: Emoti doesn't just provide businesses with segmented data; it also offers actionable recommendations on how to best reach each audience segment for maximum impact. These recommendations are based on a thorough understanding of each segment's unique characteristics, ensuring that marketing efforts resonate with the target audience and drive results.

- **Continuous improvement**: Emoti's AI-driven approach allows for ongoing analysis and refinement of segmentation strategies. As customer preferences and market conditions change, Emoti adapts, ensuring that businesses always have access to the most relevant and up-to-date segmentation data.

By addressing the perils of segmentation and offering a comprehensive, AI-driven solution, Emoti empowers businesses to harness the power of segmentation to improve their marketing efforts, better understand their customers, and ultimately drive business success.

### Summing Things Up

In today's competitive business landscape, targeting the right customers is essential for success. The art of segmentation allows businesses to identify and target their ideal customer, increasing the relevance and effectiveness of their marketing efforts. By using a combination of demographic, geographic, psychographic, and behavioural segmentation, businesses can create detailed pictures of their ideal customer and tailor their marketing efforts to reach them.

However, it is important to recognise the potential pitfalls of segmentation, such as over-segmentation, inaccurate segmentation, ignoring evolving customer preferences, and overlooking cross-segment opportunities. Emoti, an AI-driven solution, addresses these perils by automating the segmentation process, providing valuable audience insights, and offering clear recommendations on how to best reach each audience segment for maximum impact.

By implementing effective segmentation strategies and leveraging innovative solutions like Emoti, businesses can not only increase their return on investment, grow their business, and improve their products and services, but also mitigate the risks associated with improper segmentation. This ensures that marketing efforts remain relevant, focused, and impactful in an ever-evolving marketplace.


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