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Discovering the Encoded Linguistic Knowledge in NLP Models

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This article is authored by Keyur Faldu and Dr. Amit Sheth. This article elaborates on a niche aspect of the broader cover story on “Rise of Modern NLP and the Need of Interpretability!”At Embibe, we desiderate answers to the open questions while we build the NLP platform to solve numerous problems for the academic content.

Modern NLP models (BERT, GPT, etc) are typically trained in the end to end manner, carefully crafted feature engineering is now extinct, and complex architectures of these NLP models enable it to learn end-to-end tasks (e.g. sentiment classification, question answering, etc.) without specifying the features explicitly [2]. Linguistic features (like part-of-speech, co-reference, etc) have played a key role in the classical NLP. Hence, it is important to understand how modern NLP models are arriving at decisions by “probing” into what all they learn. Do these models learn linguistic features from unlabelled data automatically? How can we interpret the capabilities of modern NLP models? Lets probe.

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Linguistics: The Background

Linguistic knowledge is an essential aspect of natural language processing. We can think of it in the following dimensions,

  • Syntax: analyzing the structure of sentences and the way words are connected.
  • Morphology: deals with the inner structure of individual words and how new words are formed from morphs of these base words.
  • Phonology: the study of the system of sounds comprising speech, that constitute fundamental components of language.
  • Semantics: deals with the meaning of individual words and entire texts.

In statistical methods and classical machine learning, solving any problem related to natural language processing involves deriving linguistic knowledge described above. Thus, the research community gave attention to numerous tasks related to linguistic knowledge. We can see a few examples as below:

Figure 2: Example of linguistic knowledge in a sentence. (Image from the other article)
  • Part-of-speech: Syntactic category of words, i.e., noun, verb, adjective, pronoun, etc.
  • Constituency Trees (or phrase structure grammar): Phrase structure rules consider that sentence structure is constituency-based, and a parse tree arranges these constituents in a tree structure with constituency relation.
  • Dependency Trees (or dependency grammars): Dependency grammar rules consider that sentence structure is dependency-based, and the dependency parse tree arranges words in a tree structure with dependency relation.
  • Coreference: Relationship between two words or phrases with the common referent.
  • Lemmatization: Deriving base lemma word after removing prefixes or suffixes using morphological analysis.

Above are a few examples of important tasks related to linguistic knowledge, where part-of-speech mainly deals with syntactic knowledge, dependency trees, and co-references are important to further understand semantics, and lemmatization is an example of morphology.

Numerous other tasks further analyze the linguistic properties of a sentence, like semantic roles, semantic proto-roles, relation classification (lexical and semantic), subject noun, main auxiliary verb, subject-verb agreement, etc.

Modern NLP Models

Modern NLP models are either LSTM based or transformer based. ELMO and ULMFIT are examples of LSTM architecture based language models. In contrast, BERT [1] and GPT are examples of transformers architecture based language models. For the rest of the study, let’s take an example of “BERT” as a reference.

  • The BERT model is pre-trained with an objective of masked word prediction, and next sentence prediction on massive unlabeled data.
  • The pre-trained BERT model is fine-tuned by extending it with the task-specific layers for tasks like ‘sentiment analysis,’ ‘text classification,’ or ‘question answering’ with limited labeled data.

Representations produced by the pre-trained BERT models encode relevant information, which enables task-specific fine-tuning with very limited labeled data. The question is,

What Linguistic Knowledge is Encoded in BERT?

As a result, a flurry of research sought to understand what kind of linguistic information is captured in neural networks. The most common theme across different approaches can be grouped as “probes” (or probing classifiers, diagnostic classifiers, auxiliary prediction tasks), which probes how internal mechanisms of neural networks can classify (or perform on) auxiliary linguistic tasks (or probe tasks, or ancillary tasks).

Figure 3. The illustration diagram of Probes on the BERT model. It shows how input tokens are contextualized in successive layers using attention mechanisms. Two types of Probes are shown, (1) representation based, and (2) attention-based. Note, the diagram is for broader illustration, so special tokens like CLS and SEP are not shown.

How do “Probes” work?

  • Probes are shallow neural networks (often a classifier layer), inserted on top of intermediate layers or attention heads of a neural network trained for a primary task. Probes help to investigate what information is captured by different layers, or attention heads. Probes are trained and validated using auxiliary tasks to discover if such auxiliary information is captured.
  • Figure 3 illustrates, how probe classifiers can be inserted on top of different layers or attention heads, to discover the encoded information related to auxiliary tasks by different layers and attention heads.
  • Let say, we want to investigate if encoded representations from the BERT model capture linguistic information, like “if a verb is an auxiliary verb” or “if a phrase is a subject noun”. Auxiliary verbs are the helping verbs, and subject nouns are noun phrases that act as a subject. These tasks can be framed as “auxiliary tasks” for probes. For example, in the sentence “Kids are playing cricket all day,” — are” is an auxiliary verb, “playing” is the main verb, “Kids” is the subject noun, and “Cricket” is an object noun.
  • If a probe classifier is not able to do well on auxiliary task for linguistic information, that means such information is not encoded in internal representations of a model, also possible because it might not be needed to solve primary objectives of the model.

How are “Probes” different from Fine-Tuning or Multi-Task Learning?

Table 1. Probes vs Fine-Tuning vs Multi-Tasks Learning
  • “Probes” are not related to fine-tuning for downstream tasks neither in its goal nor in approach.
  • Table 1 shows the comparative landscape.
  • “Probes” are to discover encoded linguistic knowledge, whereas fine-tuning and multi-tasks learning trains the model on one or multiple primary tasks.
Figure 4. Multi-task learning vs Probes
  • As illustrated in figure 4, “Probes” can access model internals but can not update model parameters, on the other hand, fine-tuning and multi-tasks learning does not access model internals, but they can update model parameters.
  • “Probes” should be shallow in terms of complexity, (i.e. a single layer classifier on top of the model), whereas fine-tuning and multi-task learning can stack up deep layers depending upon the downstream tasks complexity [7][8].

What are Different Types of “Probes”?

These probing classifiers can be categorized based on what neural network mechanisms they are leveraging to probe for the linguistic knowledge. These are mainly

  • Internal Representations: A small probe classifier is built on top of internal representations from different layers to analyze what linguistic information is encoded at different layers.
  • Attention weights: Probe classifiers are built on top of attention weights to discover if there is an underlying linguistic phenomenon in attention weights patterns.

(A) Internal Representations based “Probes”:

Quite a few techniques are probing how much linguistic knowledge is encoded in internal representation at different layers of models like BERT. Let’s take a look at a couple of examples.

(A.1) Edge Probing: A framework introduced by Tenney et al. [4][5] aims to probe linguistic knowledge encoded in contextualized representations of a model.

  • For auxiliary tasks like Part-of-Speech, Constituents, Dependencies, Entities, Semantic Role Labelling, Semantic Proto Roles, and Coreference resolutions, it has compared the performance of contextualized representations of models like BERT, GPT, ELMO, and CoVe.
  • Edge probing decomposes structured-prediction tasks into a common format, where a probing classifier receives a text span (or two spans) from the sentence and must predict a label such as a constituent or relation type, etc. from per-token embeddings for tokens within those target spans.
  • The macro average of performance overall the auxiliary tasks for the BERT-Large model was 87.3, whereas the baseline probe using non-contextualized representations achieved 75.2. So, about 20% of additional linguistic knowledge was injected into as part of contextualization.

(A.2) BERT Rediscovers the Classical NLP Pipeline: Tenny et al. [3][9] further analyzed where linguistic knowledge comes from.

  • Center of Gravity: Center of gravity reflects the average layer attended to compute scalar mixing (weighted pooling) of internal representations at different layers. For each task, intuitively, we can interpret a higher center of gravity means that the information needed for that task is captured by higher layers.
  • Expected layer: Probe classifier is trained with the scalar mixing of internal representations of different layers. Contribution (or differential score) of layer i is computed by taking the difference of “performance of probe trained with layers 0 to i” with “performance of probe trained with layer 0 to i-1”. The expected layer is the expectation of differential score over each layer.
Figure 5: Probe performance, and layers contribution to auxiliary tasks (Image source, Tenney et al. [5])
  • In figure 5, row labels are auxiliary tasks for probing linguistic knowledge. F1 scores for probe classifiers for each task are mentioned in the first two columns, where l=0, indicates auxiliary tasks performance on non-contextual representations, and l=24 indicates auxiliary tasks performance by mixing contextual representations from all 24 layers of the BERT model. Expected layers are shown in purple color (and the center of gravity is shown in dark blue color).
  • The expected layer is where the maximum additional linguistic knowledge comes from. And, it can be seen that linguistic knowledge about syntactic tasks gets acquired in initial layers, and for semantic tasks gets acquired in later layers.

(B) Attention weights based “Probes”:

“What Does BERT Look At? An Analysis of BERT’s Attention,” Clark et al. [2] probe attention weights for linguistic knowledge in BERT. It was intriguing to notice how specific attention heads are expressing linguistic phenomena, and attention heads combinations predict linguistic tasks such as dependency grammar that is comparable to the state of the art performance.

(B.1) Specific Attention Heads

  • As can be seen in figure 6, specific attention heads in BERT express specific linguistic phenomena, where a token attends other tokens depending on the linguistic relation expressed by the attention head.
Figure 6: Linguistic phenomena expressed by specific attention heads in BERT. (Image source: Clark et al. [3])
  • Visualizations of six different attention heads are shown above. The BERT base model has 12 layers, and each layer has 12 attention heads. The top-left plot in figure 5 represents the 10th attention head in the 8th layer. And the patterns where objects are attending to their nouns are evident. Similarly, in the 11th attention head of the 8-th layer, noun modifiers (determiners, etc.) are attending to their nouns. Similarly, we can notice how attention heads in other plots are expressing linguistic knowledge.
  • It is really surprising to notice how attention heads perform as readily available probe classifiers.
Table 2: Dependency relation classification accuracy by specific attention heads. Clark et al. [3]

As shown in table 2, for each dependency relationship, how a specific attention head achieves classification performance of predicting dependent token. For cases like determinant (det), direct object (dobj), possessive word (poss), passive auxiliary (auxpass), etc performance gain was huge (~100%) compared to the baseline model (predicting a token at the best fixed offset).

(B.2) Attention Head Combinations

Table 3: Performance of different baselines and probe techniques. UAS is an unlabelled attachment score for dependency head token prediction. Clark et al. [3]
  • Probe classifiers trained on directly taking linear combinations of attention weights, and attention weights with non-contextual embeddings like GloVe, gave a comparable performance to relatively complex models depending on internal contextual representations for dependency parsing tasks.
  • Similarly, experiments on coreference, resolution tasks also suggested similar potential. That said, we can conclude that attention mechanisms in BERT also encode and express linguistic phenomena.

Probing the “Probes”

Now that we got introduced to representation based probes and attention weights based probes to discover the encoded linguistic knowledge using auxiliary tasks, it would be interesting to ask deeper questions:

  • Are bigger models better to encode linguistic knowledge?
  • How to check for the generalization ability of a model to encode linguistic knowledge?
  • Can we decode linguistic knowledge instead of relying on shallow probe classifier labels?
  • What are the limitations of probes, and how to draw conclusions?
  • Can we infuse linguistic knowledge?
  • Does encoded linguistic knowledge capture meaning?
  • Is encoded linguistic knowledge good enough for natural language understanding?

Lets elaborate further on the above questions in the next article Linguistic Wisdom of NLP Models.

References:

  1. Devlin et al. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”. NAACL 2019.
  2. Belinkov et al. “Analysis Methods in Neural Language Processing: A Survey”, ACL 2019
  3. Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning, “What Does BERT Look At? An Analysis of BERT’s Attention”, 2019
  4. Ian Tenney, Dipanjan Das, Ellie Pavlick, “BERT Rediscovers the Classical NLP Pipeline”, 2019
  5. Tenney et al. “What Do You Learn From Context? Probing For Sentence Structure In Contextualized Word Representations”, ICLR 2019
  6. Adi et al. “Fine-Grained Analysis of Sentence Embeddings Using Auxiliary Predictions Tasks”, ICLR 2017
  7. Stickland et al. “BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning”, ICML 2019
  8. Zhou et al. “LIMIT-BERT : Linguistic Informed Multi-Task BERT”, 2019

This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author.

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Source: https://www.topbots.com/encoded-linguistic-knowledge-in-nlp-models/

Artificial Intelligence

AI Offers Powerful Insights into Video Content Production

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AI, language processing, and deep learning are three major technologies that affect streaming services. They are already used to create content and the picture looks beautiful and detailed. If we exploit Artificial Intelligence in live streaming, this would change everything. Modern technology allows us to create automated actions, trigger real-time graphics, analyze audio, monitor social media sentiment and auto-sharing.

AI Leads to New Opportunities for Video Content Development

Some developers saw AI technology as an opportunity and created useful tools that help in video production. Let’s talk about 4 software variants that could be used for video production.

NotMP3

To process multimedia you should have a useful tool that will allow getting them without payment. Nowadays the best content provider is Youtube. That is why we decided to mention NotMP3 software, which uses cutting-edge AI technology. This sort will allow you to fulfill this task for free.

This is free software which is downloadable from the site. This is a simple but very powerful tool that can help you to get audio or video file of any quality you like. You can also get files from Soundcloud and transfer them to various formats.

The main advantage of NotMP3 software is that it is completely free and does not require creating an account. All you have to do is to install and use it.  

Transcriptive AI

The first program that actually uses AI technology is Transcriptive AI. This is a universal tool that is exploited to transcribe footage. It will make that footage searchable. You can use this software to make caption and paper edits.

The creator allows getting a free trial copy but if you want to continue using it, you will have to buy the product. This soft will be useful if you want to find some pieces of audio that have been mentioned in the video. You will also have accurate transcripts spending as little time as possible. The most powerful component is searchability – you can look for clips, sequences, and markers. This machine learning tool works like Google and you just mention the keywords.

Pixop

This is not a tool but a cloud service that exploits intuitive interface and AI technologies for video enhancement. The main advantage of this service is that it is very easy to use. You will not need any plugins and other tools. No subscription and fees are required. This service has been created to encourage creators to update their digital archives as easily as it is possible.

Users value Pixop because it provides a place where you can make video enhancement as fast as possible. This is a flexible and scalable could service which has been created to tackle every requirement. And the most important feature is the fact that it is powered by machine learning – just let AI do the work! Just sign up and use the online tool.

Imagen

This is a media management tool that is used for keeping media safe, secure, and easy to find. This program will help you to organize and unlock the full potential of your video files. You just open a video file and start editing it. This software uses a simple but effective technology to process a picture. Choose a frame, pause it and crop out the needed fragment. Then this picture could be saved on your device and used to create media products. You can add annotations for any image.

This tool could be ideal for content makers that are willing to organize and see the true value of their content. This service lets you focus on the important features of the picture. It is very good for business in content-making industry.

AI is Invaluable for Creating New Video Content

So, what is the best AI solution for content creation? At first, you will need a tool that would be able to download the high-quality video as fast as possible. That is why you will definitely need NotMP3 downloader and converter.

After that try Transcriptive AI program because this is professional software. It has lots of useful features. But it is not free.

If you are looking for the fastest way, try Pixop cloud service – it has all the needed functions. You can use Imagen as a free offline variant which will help you to create the best content.

The post AI Offers Powerful Insights into Video Content Production appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/ai-offers-insights-into-video-content-production/

Continue Reading

Artificial Intelligence

AI Offers Powerful Insights into Video Content Production

Avatar

Published

on

AI, language processing, and deep learning are three major technologies that affect streaming services. They are already used to create content and the picture looks beautiful and detailed. If we exploit Artificial Intelligence in live streaming, this would change everything. Modern technology allows us to create automated actions, trigger real-time graphics, analyze audio, monitor social media sentiment and auto-sharing.

AI Leads to New Opportunities for Video Content Development

Some developers saw AI technology as an opportunity and created useful tools that help in video production. Let’s talk about 4 software variants that could be used for video production.

NotMP3

To process multimedia you should have a useful tool that will allow getting them without payment. Nowadays the best content provider is Youtube. That is why we decided to mention NotMP3 software, which uses cutting-edge AI technology. This sort will allow you to fulfill this task for free.

This is free software which is downloadable from the site. This is a simple but very powerful tool that can help you to get audio or video file of any quality you like. You can also get files from Soundcloud and transfer them to various formats.

The main advantage of NotMP3 software is that it is completely free and does not require creating an account. All you have to do is to install and use it.  

Transcriptive AI

The first program that actually uses AI technology is Transcriptive AI. This is a universal tool that is exploited to transcribe footage. It will make that footage searchable. You can use this software to make caption and paper edits.

The creator allows getting a free trial copy but if you want to continue using it, you will have to buy the product. This soft will be useful if you want to find some pieces of audio that have been mentioned in the video. You will also have accurate transcripts spending as little time as possible. The most powerful component is searchability – you can look for clips, sequences, and markers. This machine learning tool works like Google and you just mention the keywords.

Pixop

This is not a tool but a cloud service that exploits intuitive interface and AI technologies for video enhancement. The main advantage of this service is that it is very easy to use. You will not need any plugins and other tools. No subscription and fees are required. This service has been created to encourage creators to update their digital archives as easily as it is possible.

Users value Pixop because it provides a place where you can make video enhancement as fast as possible. This is a flexible and scalable could service which has been created to tackle every requirement. And the most important feature is the fact that it is powered by machine learning – just let AI do the work! Just sign up and use the online tool.

Imagen

This is a media management tool that is used for keeping media safe, secure, and easy to find. This program will help you to organize and unlock the full potential of your video files. You just open a video file and start editing it. This software uses a simple but effective technology to process a picture. Choose a frame, pause it and crop out the needed fragment. Then this picture could be saved on your device and used to create media products. You can add annotations for any image.

This tool could be ideal for content makers that are willing to organize and see the true value of their content. This service lets you focus on the important features of the picture. It is very good for business in content-making industry.

AI is Invaluable for Creating New Video Content

So, what is the best AI solution for content creation? At first, you will need a tool that would be able to download the high-quality video as fast as possible. That is why you will definitely need NotMP3 downloader and converter.

After that try Transcriptive AI program because this is professional software. It has lots of useful features. But it is not free.

If you are looking for the fastest way, try Pixop cloud service – it has all the needed functions. You can use Imagen as a free offline variant which will help you to create the best content.

The post AI Offers Powerful Insights into Video Content Production appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/ai-offers-insights-into-video-content-production/

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AI

How AI is Transforming Cybersecurity in 2021?

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Artificial Intelligence (AI) is one of the main weapons by which companies or medium-sized corporations can combat numerous cyber threats successfully.

According to Warren Buffet, “Cyber-attack is the biggest threat to mankind, even more of a bigger threat than the nuclear weapon.” Therefore, organizations should consider applying the concepts of AI within their workplaces if they want to prosper in the future without compromising their digital anonymity.

Continue reading this post to know what is AI and how it is transforming cybersecurity for all the right reasons.  

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a modern branch of computer science. It helps create smart devices that can perform various tasks depending on the situation using the core concepts of human intelligence.

Thanks to Artificial Intelligence (AI), you can now deposit cheques online from your homes hassle-free. Furthermore, AI allows you to decipher handwriting, making online cheque processing a reality these days.

As far as the importance of AI in cybersecurity is concerned, it enables cybersecurity professionals in detecting and resolving numerous security risks residing in corporate networks of different organizations proactively.

How is AI transforming cybersecurity in 2021?

As previously discussed, AI is providing numerous solutions to cybersecurity experts worldwide. Besides, Artificial Intelligence (AI) will replace the need for human beings in cybersecurity by the end of 2030.

In short, AI will help organizations or businesses significantly improve the performance of their cybersecurity departments in the future without relying on human expertise.

As expected, various organizations are already relying on AI concepts when they want to detect and eliminate potential cyber risks within their networks timely.

Moreover, 80% of telecom companies are benefiting from AI notions to protect themselves against dangerous cybersecurity issues like hacking, data theft, identity theft, and others accordingly.

The best thing about implementing AI concepts is that companies can reduce their cost by 12% in terms of threats and breaches detection. In addition to this, they can follow use cases of AI to improve their performance cybersecurity-wise.

This is one of the major reasons why organizations or businesses are investing huge amounts of money in Artificial Intelligence (AI) globally.

Considering the significance of AI in cybersecurity, we can expect that the role of AI when it comes to enhancing cybersecurity of organizations or businesses will increase with the passage of time.

Is everything rosy with Artificial Intelligence (AI)?

There is no denying that AI offers various advantages to its users, be it companies, medium-sized enterprises and small businesses. However, the worst thing about AI is that it is easily accessible to everyone, including cyber terrorists, which is not good from a cybersecurity point of view.

Therefore, hackers can use the said concepts of AI to accomplish their malicious objectives. Unfortunately, they can access AI models and use them to bypass prevailing cybersecurity practices of organizations or businesses easily.

As a result, they can gain control of corporate IT networks and misuse official data including customers’ personal information and sensitive business communications to execute their notorious plans.

In this scenario, companies should use cybersecurity tools such as VPNs. They can try a VPN service that offers a free trial if they do not want to spend hundreds of bucks initially to see if it helps them safeguard their digital assets from hackers and other cyber goons. Once they are satisfied with its performance, they can consider using its premium packages as per their preferences.

Apart from this, there is no harm in availing different online protection tools like antivirus software, email encryption software, password managers, firewalls, KPIs, etc. Once organizations or companies start using them, they can effectively protect their official devices and business data from the prying eyes of notorious elements over the web.

Lastly, they should provide basic cybersecurity training to improve their employee’s personal privacy either remote or office-based comprehensively.

As a result, they will start updating their official devices regularly. Moreover, they can secure their devices from phishing attacks as they will not click on a suspicious link provided in emails sent from unknown people.

They can also start using password managers like LastPass and others that will enable them to protect their crucial login credentials trouble-free.

Wrapping Things Up

Artificial Intelligence (AI) does have all the right ingredients to transform the future of cybersecurity to the next level. However, companies or businesses should be smart enough to apply AI notions within their workplaces with caution.

Besides, they should not stop following certain cyber hygiene practices at any cost, like installing antivirus software on official devices, data backup, password managers, email encryption tools, etc.

This way, they can protect themselves from the consequences of hacking, data theft, malware, phishing and other cyber risks in 2021 and beyond.

The post How AI is Transforming Cybersecurity in 2021? appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/how-ai-is-transforming-cybersecurity/

Continue Reading

AI

How AI is Transforming Cybersecurity in 2021?

Avatar

Published

on

Artificial Intelligence (AI) is one of the main weapons by which companies or medium-sized corporations can combat numerous cyber threats successfully.

According to Warren Buffet, “Cyber-attack is the biggest threat to mankind, even more of a bigger threat than the nuclear weapon.” Therefore, organizations should consider applying the concepts of AI within their workplaces if they want to prosper in the future without compromising their digital anonymity.

Continue reading this post to know what is AI and how it is transforming cybersecurity for all the right reasons.  

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a modern branch of computer science. It helps create smart devices that can perform various tasks depending on the situation using the core concepts of human intelligence.

Thanks to Artificial Intelligence (AI), you can now deposit cheques online from your homes hassle-free. Furthermore, AI allows you to decipher handwriting, making online cheque processing a reality these days.

As far as the importance of AI in cybersecurity is concerned, it enables cybersecurity professionals in detecting and resolving numerous security risks residing in corporate networks of different organizations proactively.

How is AI transforming cybersecurity in 2021?

As previously discussed, AI is providing numerous solutions to cybersecurity experts worldwide. Besides, Artificial Intelligence (AI) will replace the need for human beings in cybersecurity by the end of 2030.

In short, AI will help organizations or businesses significantly improve the performance of their cybersecurity departments in the future without relying on human expertise.

As expected, various organizations are already relying on AI concepts when they want to detect and eliminate potential cyber risks within their networks timely.

Moreover, 80% of telecom companies are benefiting from AI notions to protect themselves against dangerous cybersecurity issues like hacking, data theft, identity theft, and others accordingly.

The best thing about implementing AI concepts is that companies can reduce their cost by 12% in terms of threats and breaches detection. In addition to this, they can follow use cases of AI to improve their performance cybersecurity-wise.

This is one of the major reasons why organizations or businesses are investing huge amounts of money in Artificial Intelligence (AI) globally.

Considering the significance of AI in cybersecurity, we can expect that the role of AI when it comes to enhancing cybersecurity of organizations or businesses will increase with the passage of time.

Is everything rosy with Artificial Intelligence (AI)?

There is no denying that AI offers various advantages to its users, be it companies, medium-sized enterprises and small businesses. However, the worst thing about AI is that it is easily accessible to everyone, including cyber terrorists, which is not good from a cybersecurity point of view.

Therefore, hackers can use the said concepts of AI to accomplish their malicious objectives. Unfortunately, they can access AI models and use them to bypass prevailing cybersecurity practices of organizations or businesses easily.

As a result, they can gain control of corporate IT networks and misuse official data including customers’ personal information and sensitive business communications to execute their notorious plans.

In this scenario, companies should use cybersecurity tools such as VPNs. They can try a VPN service that offers a free trial if they do not want to spend hundreds of bucks initially to see if it helps them safeguard their digital assets from hackers and other cyber goons. Once they are satisfied with its performance, they can consider using its premium packages as per their preferences.

Apart from this, there is no harm in availing different online protection tools like antivirus software, email encryption software, password managers, firewalls, KPIs, etc. Once organizations or companies start using them, they can effectively protect their official devices and business data from the prying eyes of notorious elements over the web.

Lastly, they should provide basic cybersecurity training to improve their employee’s personal privacy either remote or office-based comprehensively.

As a result, they will start updating their official devices regularly. Moreover, they can secure their devices from phishing attacks as they will not click on a suspicious link provided in emails sent from unknown people.

They can also start using password managers like LastPass and others that will enable them to protect their crucial login credentials trouble-free.

Wrapping Things Up

Artificial Intelligence (AI) does have all the right ingredients to transform the future of cybersecurity to the next level. However, companies or businesses should be smart enough to apply AI notions within their workplaces with caution.

Besides, they should not stop following certain cyber hygiene practices at any cost, like installing antivirus software on official devices, data backup, password managers, email encryption tools, etc.

This way, they can protect themselves from the consequences of hacking, data theft, malware, phishing and other cyber risks in 2021 and beyond.

The post How AI is Transforming Cybersecurity in 2021? appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/how-ai-is-transforming-cybersecurity/

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