Folks at OpenAI developed the ChatGPT model as a way to optimizing language models for dialogues. I found this idea quite challenging with a lot of potential. Therefore, I asked to Chat.OpenAI an open question that on a topic that I am familiar with. As ChatGPT is design for dialog I asked also a few sub-questions as well where I was searching for more details. What follows is the unedited answers:

What is Service Oriented Architecture

Service-oriented architecture (SOA) is a software design approach that focuses on building a network of independent, modular components that provide services to other components over a network in order to form a complete application. This architecture is designed to make it easier to develop, deploy, and manage complex applications by breaking them down into smaller, independent units of functionality that can be developed, tested, and deployed independently. SOA is often used in distributed systems and relies on communication protocols such as web services to enable components to interact with each other.

Some of the key features of service-oriented architecture (SOA) include:

1. Modularity: SOA is based on the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently.
2. Independence: Each component in an SOA-based system is independent and can be managed and maintained separately from other components, which makes it easier to develop, deploy, and update applications.
3. Standardized communication protocols: SOA relies on standardized communication protocols, such as web services, to enable components to interact with each other. This allows different components to be developed using different programming languages and technologies, as long as they can communicate using the same protocol.
4. Reusability: Because components in an SOA-based system are independent and modular, they can be easily reused in other applications, which can save time and effort during development.
5. Flexibility: SOA allows for flexibility by enabling components to be added, removed, or updated without disrupting the overall functionality of the application. This makes it easier to adapt to changing business requirements and technology needs.

Modularity

Modularity is one of the key features of service-oriented architecture (SOA), and refers to the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently. This allows each component to be managed and maintained separately from other components, which makes it easier to develop, deploy, and update applications.

In SOA, each component is called a “service” and provides a specific function or piece of functionality. For example, a service might be responsible for handling user authentication, or for performing calculations, or for storing and retrieving data from a database. By designing an application as a network of interconnected services, developers can create more modular and scalable applications that are easier to maintain and update over time.

Modularity is an important concept in SOA because it enables developers to create applications that are flexible and adaptable to changing business requirements and technology needs. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application. This makes it easier to evolve and improve an application over time without having to redesign the entire system.

Independence

Independence is another key feature of service-oriented architecture (SOA), and refers to the idea that each component in an SOA-based system is independent and can be managed and maintained separately from other components. This means that developers can work on different parts of the application without having to worry about how their changes will affect other parts of the system.

Independence is an important concept in SOA because it enables developers to create applications that are flexible and adaptable to changing business requirements and technology needs. For example, if a service needs to be updated or replaced, it can be done without affecting the rest of the application. This makes it easier to evolve and improve an application over time without having to redesign the entire system.

Additionally, because each service in an SOA-based system is independent, it can be developed using different programming languages and technologies, as long as it can communicate with other services using a standardized protocol. This allows developers to choose the best tools and technologies for each individual service, which can make the development process more efficient and effective.

Standardized Communication Protocols

Standardized communication protocols are an important aspect of service-oriented architecture (SOA), and refer to the use of specific rules and standards for enabling components to communicate with each other. In SOA, these protocols are used to enable services to send and receive messages and data to each other in order to perform their designated functions and contribute to the overall functionality of the application.

One of the most common protocols used in SOA is the web services protocol, which is a set of rules and standards for exchanging data between applications or services over a network. This protocol allows services to be developed using different programming languages and technologies, as long as they can communicate using the same protocol. This enables developers to create more modular and scalable applications that are easier to maintain and update over time.

In addition to the web services protocol, there are many other standardized communication protocols that are commonly used in SOA, such as REST, JSON, and XML. These protocols are designed to enable services to communicate with each other in a standardized and interoperable way, which makes it easier to develop and deploy complex applications.

Overall, the use of standardized communication protocols is an important aspect of SOA because it allows different components to be developed and maintained independently, while still being able to work together to form a complete application. This makes it easier to create flexible and adaptable applications that can evolve and improve over time.

Reusability

Reusability is a key benefit of service-oriented architecture (SOA), and refers to the ability to use components or services in multiple applications or contexts. In SOA, each component is called a “service” and provides a specific function or piece of functionality. By designing an application as a network of interconnected services, developers can create more modular and scalable applications that are easier to maintain and update over time.

One of the key advantages of this modular approach is that it allows services to be easily reused in other applications. For example, if a service is developed to handle user authentication, that service can be used in multiple applications without having to be redeveloped for each application. This can save time and effort during the development process, and can also help to ensure that services are well-tested and reliable.

Reusability is an important concept in SOA because it allows developers to create more flexible and adaptable applications that can evolve and improve over time. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application.

Overall, the ability to reuse services in multiple applications is a key benefit of SOA, and can help to save time and effort during development, as well as improve the reliability and flexibility of applications.

Flexibility

Flexibility is one of the key advantages of service-oriented architecture (SOA), and refers to the ability to easily add, remove, or update components without disrupting the overall functionality of the application. In SOA, each component is called a “service” and provides a specific function or piece of functionality. By designing an application as a network of independent services, developers can create more modular and scalable applications that are easier to maintain and update over time.

One of the key benefits of this modular approach is that it allows developers to make changes to an application without having to redesign the entire system. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application.

Flexibility is an important concept in SOA because it enables developers to create applications that are adaptable to changing business requirements and technology needs. For example, if the requirements for an application change over time, it is relatively easy to update or replace services as needed without having to redesign the entire application. This makes it easier to evolve and improve an application over time without having to start from scratch.

Overall, the ability to easily add, remove, or update components is a key advantage of SOA, and can help to improve the flexibility and adaptability of applications. This can make it easier to respond to changing business needs and technology trends, and can help to ensure that applications remain relevant and effective over time.

Summary (Still According to Chat.OpenAI)

Service-oriented architecture (SOA) is a software design approach that focuses on building a network of independent, modular components that provide services to other components over a network in order to form a complete application. This architecture is designed to make it easier to develop, deploy, and manage complex applications by breaking them down into smaller, independent units of functionality that can be developed, tested, and deployed independently. SOA is often used in distributed systems and relies on communication protocols such as web services to enable components to interact with each other.

Some of the key features of SOA include modularity, independence, and the use of standardized communication protocols. Modularity refers to the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently. Independence refers to the idea that each component in an SOA-based system is independent and can be managed and maintained separately from other components. Standardized communication protocols refer to the use of specific rules and standards for enabling components to communicate with each other.

Some of the key benefits of SOA include reusability, flexibility, and the ability to easily adapt to changing business requirements and technology needs. Reusability refers to the ability to use components or services in multiple applications or contexts. Flexibility refers to the ability to easily add, remove, or update components without disrupting the overall functionality of the application. The ability to adapt to changing business requirements and technology needs refers to the ability of SOA to enable applications to evolve and improve over time without having to redesign the entire system.

Does #OpenAI ( #OpenAIChat ) knows what is Service Oriented Architecture? #SOA #SoftwareEngineering #API
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Final Note (from the author) on OpenAI answering to What is Service Oriented Architecture

If we exclude the picture, the opening text and this closing text, the content is entirely generated by Chat.OpenAI using ChatGPT. The content of the answer looks sound and consistent and, from time to time, a bit redundant. In addition, the information are clearly correct and touch relevant sub-topics. Finally, strictly talking of the deepness of the content, it is sufficiently informative for a non expert but a bit useless for a domain expert as it can be classified as “a good introduction”. Is short? Does OpenAI knows what is Service Oriented Architecture? Well… the answer looks correct

If you are curious and you want to know more, at this link you can find another example of AI assisted writing

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A knowledge graph is also known as a semantic network and represents a network of real-world entities. In other words represent “reality” as a combination of objects, events, situations, or concepts taking into account the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”. The figure below can give you a visual idea of how these entities interlink among each other.

Intuitively, we can note that knowledge graphs take advantages of triples: subject-verb-qualifier in order to infer relationships among the data.

The knowledge graphs is at the core of the semantic web vision and standards like RDF and query languages like SPARQL are maintained by the w3c. A set of pointers for more information is available in this paper that I wrote specifically for interoperability in industry 4.0 and internet of things. However sections 2.3. (of the paper) give an overview of the semantic stack.

Existing tools and technologies that use knowledge graph

A core set of technologies that are currently in productions and used companies like Google, Facebook, Apple includes the following:

• Wikidata: Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. It is a free and open knowledge base that can be read and edited by both humans and machines.
• schema.org is an organization that publishes documentations and guidelines to using structured data mark-up on web-pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
• Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine’s results with information gathered from a variety of sources.

These technologies are strictly focusing on the data instead and the video below share a practical approach that focus on the data and how to combine them. Note that it does not consider any AI approach but focus on how to combine existing available data and their relationships.

However, it contains very nice examples and give an intuitive idea on how Wikidata, Schema.org and Knowledge graph works.

Research on Knowledge Graph and Artificial Intelligence

Now that we have an idea of what are the basic techniques for representing information we need to understand how we can use machine learning techniques for enriching these information and the expressiveness of the models. What follows is a set of seminars that discuss the knowledge graph from a more theoretical point of view and consider open research questions. This is the first part:

Below you can find the second part. It also contains very nice real life case studies. Including how Siri works!

If you are curious and would like to know more about the topic I encourage you in browsing the YouTube channel for more videos.

On #Knowledge Graph and #ArtificialIntelligence : how to combine data and models for better information. #BigData #DataScience
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Mentor: Francesco Lelli

Have you ever received an eMail from a friend proposing a vacation to Venice and 30 second later you stumble into an advertisement proposing cheap flights or hotels for that very same destination?  Welcome to the world of Natural Language Processing (NLP).

This thesis will investigate the limits that machines have in processing and understanding colloquial conversations. If you are curious to know what can actually be automatized by Artificial Intelligence this thesis is for you.

You will be able to touch with your own hands the technological and business limits that this technology is facing in its adoption into our everyday life as well as propose your personal ideas for improving the present state of the art.

Moreover, you will have the opportunity to master techniques of NLP for parsing and extracting semantic information from any kind of text.

Your claims will be validated using popular NLP libraries (yes, you will have to write a few line of code!) using an existing dataset as benchmark (no, you do not have to collect data!).

 If you are curious and you what to know more about the topic, I recommend you the following:

• A generic article in Wikipedia about Natural Language Processing: https://en.wikipedia.org/wiki/Natural_language_processing
• Watch this YouTube video (knowledge of Python will help you in understanding it)

• A few keywords that you may want to use in google scholar: natural language processing, NLP topic modelling, natural language processing interview, natural language processing data.

This project does not involve an internship. Instead, it will try to have a high academic relevance and theoretical contribution and, based on the quality of your work the candidate may be able to publish the results in the proceedings and scientific journals. If this topic triggers your intellectual curiosity, let me know.

If you are interested in the topic, do not hesitate to contact us for more information.

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Mentor at TiU: Francesco Lelli 

Mentors at Talpa: Anca Dumitrache and/or Ricardo Fabian Guevara

There are some internships opportunities available at Talpa. You will have the opportunity to develop your master’s thesis in collaboration with the AI division of one of the most innovative media companies in the Netherlands .

If you have a go-get attitude with the desire to expand your knowledge and expertise in the area of big data and artificial intelligence, this is probably the internship that you are looking for.

Topics for this thesis include Natural Language Processing (NLP), Machine Learning and Predictors. At the same time you are encouraged to propose your own idea as well.

Knowledge and Skills:

Programming, preferably Python, and/or statistical skills.

Interested in Joining Talpa?

Here you can find a short presentation of the company:

Where to Read More:

The following ideas are possible internship opportunities and related topic for your thesis. You may want to use them for forming an idea of the kind of jobs that you will be doing at Talpa as well as for developing your research proposal (here you can find a few tips for that).

Swimlane for Trending on Social Media

Both KIJK (video streaming platform) and JUKE (audio streaming platform) present their content separated into swimlanes on the front page. For instance, one swimlane contains a list of TV shows that were popular in the previous days. We would like to create a new swimlane that contains items that were trending on social media. Taking Twitter as a data source, the project will go through the following steps:

• (1) extract tweets about popular TV shows and/or radio shows,
• (2) perform entity linking to match them to the shows in our database,
• (3) aggregate the results to get the most popular shows in one swimlane.

Project: KIJK and/or JUKE

Automatic Teaser Tweet Creation

Starting with a (textual) description of a TV show episode or radio program, we would like to generate teaser tweets about the show that are meant to generate anticipation on social media. The underlying task would be a summarization problem, where the program description is mapped to a short tweet about it. The tweet should contain relevant information about the program, but not reveal any spoilers. Either an extractive (entity + relation extraction) or abstractive method could be applied.

Source: https://www.aclweb.org/anthology/N19-1398/

Project: KIJK and/or JUKE

Exploring Pair-wise Learning-to-Rank

Talpa current recommender system uses ALS, a point-wise learning-to-rank approach, where the learning objective is based on modeling the score of a given item (i.e. similarly to how regression works). Alternative methods of doing learning-to-rank are pair-wise (learning objective is to model the ranking of a pair of items relative to each other) and list-wise learning to rank (learning objective is calculated over the entire list of items). The project goal is to investigate different learning objectives and find out:

• (1) how they perform relatively to the point-wise method,
• (2) if there are subsets of data where this method works better/worse.

Source:https://medium.com/@nikhilbd/pointwise-vs-pairwise-vs-listwise-learning-to-rank-80a8fe8fadfd

Project: KIJK and/or JUKE

Automatic Playlist Generation

JUKE music player features a lot of non-stop music playlists that are manually created by an editor, usually selecting music from a given genre (e.g. hard rock non-stop radio). We would like to see whether these playlists can be generated by AI and what the quality is. This can be approached as a song clustering problem, where the feature space could contain the genre, artist, as well as other audio features.

Project: JUKE

Google AdWords for Video

Advertisers can buy Google search keywords to show their ad in conjunction with them. We would like to see whether it is possible to do something similar within videos, too. In this way, contextual advertising in different medias becomes feasible. The steps involved in this project are:

• (1) generate textual metadata of video (either based on transcript, or other features of the video; there might be some existing video metadata as well),
• (2) match video textual metadata with ad keywords.

Exploring Seasonal Trends as RecSys Features

Our domain experts know that both video and audio streaming trends are highly influenced by seasonality (e.g. Sky Radio becomes very popular over Christmas). The goal of this project is to:

• (1) identify these trends by studying user listening data, then
• (2) incorporating these trends into our prediction model (e.g. recommend Sky Radio to fans of Christmas music, but only over the Christmas holidays).

Challenge: a lot of data may be needed to accomplish this.

Project: KIJK and/or JUKE

Radio Station Embeddings

Goal: produce embedding with various sizes similar to Glove but for radio stations that reflect radio station similarity with euclidean distance.

Value: many of Talpa challenges, like radio to radio similarity, could be tackled with this approach. Our RecSys can also use the radio embedding as a feature to determine if a radio channel is a good match for a user. This could also greatly alleviate the cold start problem when we introduce new radio stations.

Approach: this is an unexplored problem but there has been previous research and practical work done on audio embeddings. In the simplest form, a radio station embedding could be just a bag of features like genre frequencies, languages and origins of artists, but also properties from the target audience like age range. Going beyond that, it could use an average of the embeddings of representative songs on it in a given past period. Another valuable asset of Talpa is its understanding of usage behavior, so radio station features could also be calculated based on the type of person who listens to it, the frequency of listening and other seasonal behavior.

At the same time, there is a temporal aspect to take into consideration when analyzing user preference of radio stations. Therefore, we cannot expect the embeddings to remain the same forever. Consequently a refresh period is to be expected (even word embeddings need to account for word semantic drifting, only their refresh window is larger).

Project: Juke

Podcast Embeddings for JUKE

Similar to the radio embeddings project, Talpa is interested in creating embeddings for the podcasts available on JUKE. Podcast recommendations suffer from cold start problem even more than radio, since there is usually a high volume of items that are published continuously. The features that can be used for podcast embeddings are also slightly different than typical audio embedding features based on e.g. musical genre.

Project: Juke

Interested in a Talpa internship? Check also the Following:

Webinar about Talpa Internship Opportunities (Available for Tilburg Student Only)

Interested in Applying?

Send an email to Francesco Lelli with CV, Short motivation letter, project(s) that interest you and (optional) draft research proposal. In the case you would like to propose a particular project please put extra attention to your research proposal.

A selected list of candidates will be interviewed by TALPA that has the ultimate saying on accepting you for an internship. Succes, Good luck, In Bocca al Lupo!

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