Ithaca (DeepMind)

What is Ithaca?

Ithaca is a software or an algorithm developed by the British company DeepMind, which uses Machine learning is intended to complete fragmentary ancient texts. To this end, the programme is primarily used in epigraphy, the science of inscriptions on various materials. Currently, the algorithm is aimed at the analysis of ancient Greek texts, but is In future, the application is also planned for other languages.

In addition to filling in the gaps, Ithaca should also provide information about the place of origin as well as the date of composition of the texts. The dating of these texts was previously not possible with common methods such as the radiocarbon method, because for this application the texts had to be written on carbonaceous materials.

The algorithm was developed by the company DeepMind (AlphaGo, AlphaZero, AlphaFold) was developed in cooperation with several companies such as Google and universities such as the University of Oxford, the Athens University of Economics and Business or the Università Ca'Foscari di Venezia. The name Ithaca was chosen as a homage to the homonymous home island of Odysseus from Homer's epics.

In order to make the algorithm accessible to as many people as possible, DeepMind has published the source code on the Open Source version management platform GitHub, which can be used and further developed there.

What are the functions of DeepMind's new ML model?

The algorithm for Text completion works - like most of the algorithms which artificial intelligence Apply - on the basis of probabilities. To do this, Ithaca uses the largest digital database for ancient texts from the Packard Humanities Institute in California to calculate the words that most likely fit into the gaps. The Database comprises almost 180,000 inscriptions, all of which are provided with metadata such as place and time.

Based on this, the calculation result is presented to experts for final determination of the text gaps, who evaluate the original text with their expertise. In an experiment conducted by DeepMind, Ithaca alone was able to recover single words with an accuracy of 62 %, but historians were only able to do so with an accuracy of 25 %. By collaborating the assessment of the programme and the experts, the accuracy to recover missing words increased to 72 %.

The "Lexicon of Greek Personal Names" (LGPN), a lexicon of Greek names from the British University of Oxford, supports the dating and classification of the region in which the respective writing was produced. With this data, it can be analysed when which names were most frequently represented in which region, thus generating insights into the geographical and temporal classification of the ancient texts. In this way, Ithaca should achieve an accuracy of 71 % for the date and lie within a time span of less than 30 years for the proposed date of origin.

How can researchers access Ithaca from DeepMind?

The algorithm can be accessed in several ways:

  • On the open source version control platform GitHub is the source codeThe libraries and other integration options for using Ithaca are also listed.
  • Ithaca was founded in a Contribution of the trade journal Nature published. In the publicly available contribution, reference is made to the The working principle of the neural network. In addition, the methods used are described in detail and examples are presented. The connection to the previous text retrieval based on neural networks from DeepMind called Pythia is also formed.
  • The algorithm is furthermore about the Ithaca website of the DeepMind company retrievable and is made freely and publicly available to researchers. There, the ancient Greek text can be entered using a text box, in which the missing passages are restored. In addition, a dating and a localisation of the place of publication is carried out.

Intelligent agent

What is an Intelligent Agent?

An intelligent agent (IA) in the Artificial Intelligence (AI) is a programme that can make decisions or perform a service based on the environment, input and experience. Intelligent agents are used to autonomously collect information on a regular programmable schedule or on demand by the user in real time.

Such agents are also called bots. This technique, where information is delivered by an agent, is called push technology.

There are different types of intelligent agents, such as Reflection agents, model-based agents, goal-based agents, utility-based agents and learning agents. These types of intelligent agents are practically defined by their range of capabilities and functions. Examples of these agents are Alexa and Siri. These use sensors to communicate with the user.

The Intelligent Agent Architecture has a combination of agent functions, architecture and agent programmes. This architecture is a machinery on the basis of which the agent performs its actions. Essentially, it is a device in which there are embedded actuators and sensors. For example, autonomous vehicles exist with motion and GPS sensors. There are also actuators based on inputs that support actual driving.

Where is IA used in artificial intelligence?

Widespread techniques in which AI is successfully used include industrial robots and automated production facilities, quality assurance, with Automatic image recognition, and with Speech recognition and Speech extractionand also in weather or stock market forecasts and in knowledge-based expert systems.

Types of Intelligent Agents

Types of IA include:

  • autonomous,
  • cognitive,
  • communicative,
  • modal adaptive,
  • active,
  • reactive,
  • robust
  • and social agents.

Intelligent agents are characterised by knowledge, the ability to learn and the ability to make inferences. These agents also have the ability to change behaviour.

An intelligent software agent can act flexibly. It is reactive, proactive and social. Such an IA can act autonomously in its environment. It performs its tasks on behalf of a user or other agents.

An Intelligent Agent exhibits the ability to use task-oriented problem solving through an autonomous, reactive and goal-oriented application of appropriate Artificial Intelligence methods. It uses a corresponding knowledge representation in its environment with the associated actions and goals. The IA uses logical reasoning and heuristic solution seeking when planning. He uses machine learning and can handle uncertain knowledge. Intelligent interaction is possible with it, with visualisation and natural language dialogue.

There are simple reactive agents, status-based reflex agents, practical reasoning (planning) agents and learning agents. In social agents, there are robust and distributed collaborations with various other agents for individual or shared tasks and goals. And in a multi-agent system, interacting agents pursue distributed problem solving. There is hierarchical task distribution and emergent solution behaviour (with swarm intelligence). There is also coordination in the system, with communication between the agents and with different cooperation models.

ImageNet

What is ImageNet?

ImageNet is a database that is used especially in research. It contains images that are assigned to nouns and arranged in a hierarchy. There is one noun for every 500 images, and over 14 million images are integrated in the database. Furthermore, there are well over 20,000 different English-language categories.

Areas of application

The project enjoys great popularity in various research projects. Already in 2009, this was brought out and is used for training purposes in the field of the Convolutional Neutral Networks applied. The ILSVRC software competition, which has been held since 2010, is used to correctly classify or determine scenes and various objects. Especially when it comes to machine learning, ImageNet should not be missing as a mention in this context.

Images and term database

In principle, it is a matter of displaying the correct symbol or image for a certain term. In order for the system to recognise which noun or which term belongs to the correct image, programmers can create associations and comparisons, always in connection with the respective class. In a normal standard search engine, the system is similar. Users only find information on the term they have previously entered. To ensure that pictures of the noun searched for are always displayed, the Database always be up to date.

ImageNet as Artificial Intelligence

As an independent database system, users can access a complex service that displays various results for a single search term, but all of which are related. When smelling a certain odour, it generates and activates several mental images in the human brain, as well as associations and memories that were learned, acquired and made at some time in the past life. This then results in a certain reaction.

It is similar to this with ImageNet as artificial intelligence. By feeding in various images and associated information, as well as the frequency with which certain search terms are entered, the system can not only determine the popularity of these, but also create its own ranking as well as links to similar words.

Future

ImageNet is a platform and database that never stays at one and the same level. Constant updates and extensions ensure that images and nouns or search terms are always being added that the system can work with. The database expands, i.e. continues to develop. Similar to the human brain, structures and the intelligence network are constantly being expanded. Even if the database is dependent on additional input, it can hope for a rosy future due to the great popularity and demand from industry and research.

At the same time, normally large data collector services may also pose a danger. Everything you publish and use there will somehow and somewhere be evaluated by someone and used and analysed for purposes of various kinds. The collection of statistics and market research purposes can be just some of the aspects. Major search engines work according to a similar principle. Unfortunately, there is often a lack of transparency for laypeople and normal users as to what happens with the data.

Internet of Things (IoT)

The Internet of Things refers to the concept of an increasing number of electronic devices connected to the internet and other connected devices. The IoT is therefore a vast network of connected devices that are able to exchange data and thus communicate with each other. Due to the fact that the IoT has the potential to make human influence in certain processes redundant, it is one of the most important pillars of automation. Connected devices can be used almost everywhere.

By the end of 2018, around 22 billion devices were already connected to the IoT. This number is expected to rise to around 50 billion by 2030. xxii5 Some of these devices can only collect and transmit data (sensors), some can also receive data and perform an action on it (actuators) and some can do both. 

IBM Db2

This is a database product from IBM. It has AI-driven features to help you revolutionise the control of organised and free data through multi-cloud settings. While helping you make data entry easy, it will enable your business to generate AL value. IBM's Db2 is designed to collect, explore and recover data in a proficient manner. Now you can understand the innovative speed of the Db2 product for managing assimilated data. 

Originally, Db2 products were essentially developed to run in the IBM Db2 platform. Later, the Db2 server was invented, known as the Universal Database, which is suitable for all predominant operating systems such as Linux, Windows and UNIX.

Available Db2 versions 

The current version of the Universal Database in Db2 is 10.5, which has an SSL card to speed up performance. Below are other current Db2 versions with their corresponding names. 

  • 8.1 for Stinger 
  • 9.1 for Viper 
  • 9.7 for Cobra 
  • 10.5 for Kepler 

Other versions are: 3.4 (Cobweb), 10.1 (Galileo).

Updated data server versions and functions 

Based on the need for some sophisticated features of Db2, administrations can select any applicable Db2 editions from IBM. Below are various Db2 editions and their associated features. 

Server edition and version 

This edition is suitable for all medium to large enterprises. It can run on the following platforms: Linux, UNIX and Windows. 

WSE 

This server edition is intended for workgroups as well as for the transitional management of companies.  
Through WSE, you can use High Availability Disaster Recovery and Online Reform Clean XML Web Service to support standardised alliances (Db2) and SQL imitation gridlock compression.

Express C 

Express C includes all Db2 functions, free of charge. It can be adapted to all physical or cybernetic structures. Express Imprint was developed for beginners and is suitable for medium-sized companies.
It has all the features of the Db2 data server, but its use is still limited. This edition has appropriate web service alliances, standardised associations as well as SQL gridlock compression. 

Developer Edition 

This edition is intended for a single request developer. It is suitable for designing, constructing and modelling the RFQs for deployment on any IBM Db2 server. This software cannot be customised in application development.

Why was the Db2 upgrade necessary? 

To fix the following: AD system errors, SSL certificate related problems, Db2 restore command.