A Guide to AI Tool Libraries: Where to Find the Perfect AI?

18

October

2023

5/5 (1)


What Are These Databases?

With the rapid growth of the ever-expanding universe of AI technologies, a number of tools were designed to tackle various tasks, from data analysis to content creation and beyond. This explosion of tools makes finding a perfect solution for your needs sound like an exhausting and lengthy task. Navigating this landscape can be overwhelming, this is where AI tool databases come in. These libraries of a kind serve as centralised repositories created to help users find the right AI solution for their specific needs. In this post, I’ll explore some popular options that I have had an experience with and share why “There’s An AI For That” (TAAFT) is my personal favourite.


The Options and What They Offer

1. TopAI.Tools. Established in 2019 and with a current database of 5,110+ tools, TopAI.Tools’ main focus is on displaying various AI tools tagged based on area of expertise and value. I found the offered interface to be rather user-friendly but the platform primarily centres on presenting a list of tools and lacks more extensive smart search functionalities, description and information on each tool.

2. FutureTools.io. Launched in 2020, FutureTools.io currently offers a database of 2,323 tools. It focuses on organising AI tools into categories, provides basic descriptions as well as information on pricing models and shows user ratings. However, it falls short of offering detailed search features.

3. Phygital Plus Library. In existence since 2021, this platform is limited in scope, with only 1,600+ tools, and mainly focuses on AI tools related to digital and physical products. It doesn’t offer as much information or user reviews compared to others but has an extensive search filtering feature.

4. There’s An AI For That (TAAFT). Founded in 2018, TAAFT boasts the most comprehensive database of over 8,831 AIs available for more than 2,311 tasks. It also has a smart AI search functionality that allows users to find the best AI tools for any use case quite effortlessly.


The Winner

As I mentioned previously, There’s An AI For That is my absolute favourite, and let me tell you why: 

1. Comprehensive Database: With a huge database of almost 9,000 tools, TAAFT has the most extensive list of AI tools, making it a one-stop shop for all your AI needs.

2. Smart Search: The platform’s smart search functionality is a game-changer. It allows the users to filter in the best possible way and find the most suitable tools quickly.

3. User-Friendliness: TAAFT’s interface is the most intuitive which makes it incredibly easy to navigate through a plethora of options.


Conclusion

Navigating through the world of AI tools can be daunting but databases like the above make the journey much more enjoyable and easy. While each of these databases has its benefits, “There’s An AI For That” definitely stands out. I hope my personal recommendation will serve as a useful guide in your own AI journey! Time to explore!

Feel free to share your thoughts and experiences with these AI tool databases in the comments section below!


References

1. There’s An AI For That. https://theresanaiforthat.com/

2. TopAI.Tools. https://topai.tools/  

3. FutureTools.io. https://www.futuretools.io/  

4. Phygital Plus Library. https://library.phygital.plus/

Please rate this

Who owns the data?

13

October

2022

5/5 (1)

Data is used to support and shape business strategy. In addition, it can strengthen various business processes. It is therefore logical that more and more companies see value in obtaining and analysing data. They must be protected as we would any major asset. But who actually owns this collected data, the subject about whom the information is collected, the company generating the data, the person collecting the data or the person processing the data? What rights does this include?

Copyright

A database, as stated in Art. 1(2) Database Dir. (96/9/EC), is a collection of independent works, data, or other materials arranged in a systematic or methodical manner and individually accessible through electronic or other means. It is important to note that this does not refer to the individual components of the dataset, but to the protection of the entire database. A database may be protected by copyright, as by the selection or arrangement of their contents they constitute the author’s own intellectual creation and are therefore original. The author, who created the base, shall have the exclusive rights to reproduction, distribution, communication and adaptation.

Database rights

Since 1996, a guideline has already been made which can protect a database, which is not protected by copyright, against extraction and/or re-utilization from insubstantial parts of the dataset. This guideline is called the Sui Generis Right, Art. 7(1) Ddir (96/9/EC). Instead of authorship, this guideline examines the extent to which, in a qualitative or quantitative sense, there is a substantial investment in the field of obtaining, verifying and presenting the data. In the case BHB/William Hill (2004, case C-203/02) it was clarified what is meant by substantial investment and the concept of whole/substantial part. The BHB organization conducts horse racing in Great Britain and has a database on this. William Hill offers off-course bookings using two websites that display a small portion of the database’s content. The case concerned the possible infringement of BHB’s rights by William Hill posting and using the information obtained from the BHB database on the William Hill websites. Ultimately, the Supreme Court’s ruling is that substantial investment does not cover the resources used to create materials that make up the contents of a database. In this context, this means that the drawing up of a horse list by BHB was not seen as the creation of a database but as the data itself. But a by-product of its main activity, organizing horse racing. It was further explained that there is lawful use of a database as long as the cumulative effect of repeatedly retrieving/reusing data cannot largely reconstruct the content or may prejudice the copyright holder.

Consequences

Due to the elaboration of the EU database directives, the scale and scope of database protection under the EU database directives are more limited than expected, especially for sole-source databases. In addition, calling on a database can be complicated because the explanation of the concept ‘substantial investment’ is interpreted in a limited way and finding out in which part of a database a company has invested a lot is difficult. How do you think international law will change as a result of emerging technologies?

Reference

F.C. Folmer, ‘Arrest British Horseracing Board/William Hill: het einde van de spin-offtheorie in het databankrecht?’, NtER 2005-3/4, p.

Please rate this

A comparison between SQL and NoSQL

29

September

2016

No ratings yet. A comparison between SQL and NoSQL

Huge amounts of data are collected these days, which creates the need for advanced storage technologies. Since databases were introduced in 1960’s, different types have been developed. SQL (Structured query language) is a well-known database that has emerged in the 70’s, but one of the technologies that has gained particular attention since the late 2000s is a NoSQL database. (Hadjigeorgiou, 2013). I will make a comparison between these two databases and discuss the advantages and limitations of each.

Let me start by explaining what SQL and NoSQL databases actually are: the basic concept of SQL is that it is a relational database. This means that all data is stored in relations, structured in a set of tables with columns and rows. The columns define data categories, and each row is contains a unique instance of these defined categories (Khan, 2011).

NoSQL is developed in response to the high volume data that is being created, stored and analysed by users and applications. NoSQL combines a selection of different database technologies and are non-relational databases, meaning that it does not require fixed table schemas. (Planet Cassandra, 2015). The non-relational databases can generally be divided into three categories: the document model (organizes data as a collection of documents) the graph model (data stored using nodges, edges and properties) and the Key-value Wide column models (data stored as attribute name or key with its corresponding value) (MongoDB, 2016)

A downside of a relational database might be that data has to fit in a table. The same table cannot be used to store different information, which introduces complexity issues in case of adding or restructuring data. However the table ensures a strict data storage, limiting consistency issues. (Planet Cassandra, 2015). The advantages of a NoSQL database are that it is able to process a large amount of unrelated and unstructured data, enabling developers to cope with the growing amount of data velocity, variety, volume and complexity. Because of their simpler data models, NoSQL are also able to process data faster than SQL databases. (Khan, 2011). However, NoSQL databases are less reliable compared to SQL databases because of less reliability and less data-integrity, making SQL databases preferable when data-integrity is essential (Buckler, 2015).

So which one is better, SQL or NoSQL databases? The choice should depend on the particular problem that one wants to solve. Both have their advantages and limitations and hybrid solutions may even be more suitable in some cases than eliminating one of the two.

 

References:

  • <http://www.thewindowsclub.com/difference-sql-nosql-comparision>
  • https://www.sitepoint.com/sql-vs-nosql-differences/

Please rate this