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Tag: Artificial Intelligence

Role of Peer to Peer Networks in Creating Transparency and Increased Usage of AI:

Posted on by Imarticus Learning
Reading Time: 2 minutes

With Amazon’s facial-recognition, face-IDs, use of facial-recognition at airports and on smart-phones, the police use of TASERs to immobilize suspects, and voice-cloning apps, the peer-to-peer networks aim of creating a transparent data system through increased usage of AI seems to have been accepted widely.

Artificial Intelligence applications have scored for their ease of operations; quick and unbelievable data-processing, identification capabilities, and flexible application amend-abilities.

The question of transparency has however been oft-discussed and flouted with impunity in instances of protecting privacy, ethical, legal and misuse issues. Selling of data to third parties, forced use of facial recognition, misuse of voice-cloning, and excessive use of TASERs did not result in data accountability. It appears to have become a nagging fear of constant governmental-surveillance and has come close to defeating the very purpose of its creation of transparency.

The following trends in 2018 may be important in the use of AI and transparent use of data which presently globally governments, countries and companies are vying to harness and control.

AI becomes the political focus

Some argue AI creates jobs while others claim to have lost work because of AI. A case in point is self-driven trucks and cars where more than 25 thousand workers become unemployed annually as per CNBC reports. The same is true in large depots working with very few employees. If the 2016 campaign of President Trump was about immigration and globalization, the midterms of 2018 would focus on rising unemployment due to the use of AI.

Peer-to-peer transparent networks will use blockchains

ML and AI used together are becoming useful in apps like Google, Facebook etc. where computing power and enormous data is processed in fractions of seconds to enable decision making. However, transparency in the decision making has been under a cloud and not in the control of users.

Peer-to-peer networks using block-chain technology transformed the financial sectors and are set to revitalize small industries and financial organisations functioning transparently. Ex: Presearch makes use of AI peer-to-peer networking to induce transparency in search-engines.

Other interesting trends using peer-to-peer networks and AI set to overhaul efficiency, transparency, productivity and profits are

Logistics and deliveries efficiency set to increase.
Self-driving cars rock.
Robo-cops will take-on action.
Content creation through AI.
Consumers and technology to become buddies.
Data scientists will rule in demand over engineers.
ML to aid and not replace workers.
AI will aid the health sector development.
The use of Siri, Alexa, and Google Assistant show that they use AI which currently understands advanced conversational nuances. The creation of robots, chatbots and such have raised questions of immortality, displacement of workers, and whether ML can be controlled at all to get machines to do what we humans tell them to do? It has become an issue of human wisdom vs AI- intelligence debate. Morality issues, misuse of intelligence, and subjective-experiences in humans allow us to feel, be ethical and transparent in use of AI intelligence data.

In conclusion, one must agree that the increased use of peer-to-peer networks, AI, ML, data analytics and predictive technologies are here to stay and can lead to increased transparency in data-transactions across sectors. Human wisdom and morality will be the traits that set us, humans, apart from our intelligent creations whose data-processing and learning capabilities and potential can fast spin out-of-control when these traits are not used to restrain AI.

What Are The Machine Learning Use Case in IT Operations?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

Machine learning and AI can together take your enterprise to better productivity and efficiency which translates to better profitability.ML is used to make the algorithmic programs executable and uses many ML tools. The oldest tool for ML is Shogun. Without ML none of the tasks in an AI device can run.

ML’s iterative capacity is crucial as the ML trained models independently adapt to new data. Their self-learning ability akin to the human brain learns from previous experiences and computations to give repeatable, accurate and reliable decisions and results. With the advent of data analytics, the increased dependence on AI devices and data being generated by the nanosecond Machine Learning Training has gained popularity and impetus in the last decade.

Why training is crucial:

Nearly all employees are short on workplace skills and need training in these weak areas whether it be learning a new language, an advanced technique in your expertise area, or plain communication and change management. A Machine Learning Course program helps assimilate best practices in the skills required at your job. It also progresses your career to the next level with certification. Departmental training programs improve your morale and bring all employees with similar knowledge and skills on a single platform.

ML use cases for IT operations:

For sheer want of space and time, we shall discuss such cases superficially with the intent of defining the areas where ML is beneficial for the IT operations. Some of the complex areas where ML is used are:

RPA-Robotic Process Automation:

One can undertake digitization of processes in a few months instead of many years with legacy systems. The legacy system need not be replaced since ML ensures the bots can operate on such a system based on your ML algorithm. This includes all processes from existing process analysis, RPA bots programming, and humans using the bots. The implementation time is greatly cut down and the cost of replacing legacy systems avoided. Take a look at such times in the below chart.

That having been said, it is also necessary to be aware that process complexity determines analysis and programming times, and the automation levels and time-taken for human interaction with the bots can vary. Other factors that affect the time are the training through a Machine Learning Course for the bot-interacting employee and error testing.

Predictive maintenance:

Predictive maintenance to minimize operational disruptions is another crucial area where ML helps.  The uptime and maintenance costs are the factors directly impacted by the regular maintenance of robots, bots, and connected machinery. In business parlance, this translates into cost savings of millions of dollars. A Nielsen study shows that some industries suffer downtime costs of 22,000 USD per minute.

Manufacturing/Industrial analytics:

Many industrial assets like Chillers, Boilers, Batteries, Turbines, Transformers, Valves, Circuit Breakers, Generators, Meters, and Sensors are all connected to the ML through IoT platforms. Popularly referred to as industrial-analytics ML helps reduce maintenance costs, manufacturing effectiveness and reduces downtime throughout the system from production to logistics.

Supply chain and inventory optimization:

ML leverages the optimization of such processes to the next level greatly reducing supply chain costs while increasing the organization’s efficiency and productivity.

Robotics:

ML helps automate physical logistics and manufacturing process by introducing automated advanced robotics. The resultant effects are improved effectiveness and time-saving.

Collaborative Robot:

Cobots use ML to achieve automation with a flexible process. The Cobot’s ML process, engineers the automated response of flexible robots to learn from past experience and mimicking.

Qualitative benefits:

Some of the benefits of using ML-enabled use cases are: 

  1. Better performance.
  2. Improved production continuity and rhythm.
  3. Increased worker productivity.
  4. Increased availability of time for repair and maintenance work.
  5. Better team preparation and interventions.
  6. Effective management of inventories and spare parts.
  7. Reduced costs on energy.

A study conducted by McKinsey reports that ML has the potential to use cases and provide the following benefits.

  1. Downtimes reduced by 50 percent.
  2. MTBF increased by 30 percent.
  3. The useful life of machines upped to 3-5 percent.
  4. Inventories in spares cut to 30 per cent.
  5. Maintenance costs declined by 10-40 percent.
  6. Injuries to the workforce declined by 10-25per cent.
  7. Waste reduces by 10-20 percent.
  8. Advanced analytics reduces environmental impact, improves employee morale and customer satisfaction.
  9. Betters product quality and improves performance.

In parting, if you want to do a Machine Learning Course to effectively learn ML applications try Imarticus Learning. Their machine learning training fast-tracks your career with widely-accepted ML certification. For more details in brief and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

What Are The Best Courses For Cyber Security Using Machine Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What Are The Best Courses For Cyber Security Using Machine Learning?

Today everything is online and from such activity the sheer volumes of data generated, its management and security from misuse is a matter of concern that cybersecurity professionals are tackling on a war footing.

ML and AI have seen huge developments in the last decade in conjunction with the rapid growth of data and data analytics. Most organizations value their data and ML as organizational assets. So any threat to them or the devices connected to the algorithms is considered a serious cybersecurity threat. And cybersecurity depends more and more on ML since it holds potential for analyzing large volumes of data, structure and process data in real-time, and present instantly any threat to its intelligence as it occurs.

To learn machine learning is the mainstay of threat intelligence which alerts you so you can deal with mitigating the threats. Gone are the days of incoming alerts and handling attacks. Today we have advanced ML where threat intelligence is the buzz word.

Why? Because, ML has huge applications in helping organizations defend against malware, apply TI (threat intelligence), make unknown connections, identify key parameters, the transformation of unstructured text, threat actors, and such relevant risks.

Cybersecurity ML algorithms and software help: 

  • Understand why and how ML will affect the future of cybersecurity
  • AI techniques add value to ML to make the analysts more effective.
  • Obtain insights on ML processes for threat intelligence.
  • Help to detect future threats through predictive analytics.

How to become a cybersecurity professional:

Choosing a career in cybersecurity or opting to change careers to it is a great career move at the moment. You will have to learn machine learning with a reputed training institute like Imarticus Learning who are renowned for fast-tracking career options and enhancing technical skills required for careers in the latest emerging fields. Such learning courses are available online with a host of reading and comprehension on cybersecurity risks and its mitigation. However, classroom sessions and supervised learning will also be needed to gain practical and implementation skills.

You could start with an entry-level position gaining experience in security, risk management or IT and move your way up to a mid-level role as an analyst, security administrator, risk auditor or cybersecurity engineer. To sharpen and hone your cybersecurity skills advanced training and certifications will be required before you can actually practice as a security consultant.

Cybersecurity education:

A formal after-school college experience for an associate’s degree will take four full-time semesters or two years to start as an Intern. A bachelor’s degree could last 8-semesters or four years and the master’s degree will last two years or another 4-semester duration should help you learn all about the theory behind cybersecurity.

The actual practice of writing algorithms can be honed by online challenges participation, certifications and hackathons on Kaggle. The necessary attributes for cybersecurity would be proficiency in English, mathematics, and statistics. Combined with a certification you are set to start your career according to the BLS.

The top-10 roles in Cyber Security:

The field of risk evaluation, mitigation and prediction are growing with data analytics and data taking center stage in modern times. Take your pick of career paths from a few of the roles enumerated here to always be in demand.

  • Ethical Hackers.
  • Security Systems Administrator.
  • Security Consultant.
  • Computer Forensics Analysts.
  • Information Security Analyst.
  • Chief Information Security Officer.
  • IT Security Consultant.
  • Penetration Tester.

The top cybersecurity certifications:

Certifications are essential to your resume and offer employers a real-time measurable scale of your skills in cybersecurity and validate that you can implement and use the learn machine learning applications for risks and security of cyber systems effectively and practically.

The top certifications are: 

  • The ISC (2) Certification
  • CISM- Information Systems Manager
  • CISA- Information Security Auditor
  • Information Systems and Risk Control Certified
  • CEH- Ethical Hacker
  • Tester GIAC/ GPEN Penetration
  • Cyber Security Courses     State Approved

Payouts:

The Cybersecurity professionals have a median salary of 116,000 USD. At an hourly rate of 55.77 USD/ hour, it is almost thrice the national average income offered to full-time workers. The BLS reports make the high salaries a very attractive feature to make cybersecurity your dream career.

Conclusions:

The cybersecurity professional is highly paid and has immense job scope in a variety of roles. Formal education, practical skills, certification and performance at the end-of-the-day will set you apart and help your career progression. There definitely is a huge demand for the cybersecurity professions which will continue into the next decade according to most reports on Glassdoor, Payscale, and BLS.

Resources are available aplenty to make cybersecurity your career no matter where you live. The Imarticus Learning courses, unlike many online programs, have limited class sizes meant to enhance learning, certification attainment and networking.

So hurry and learn machine learning! Also, for more details and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

How Criminals Are Using AI and Exploiting It To Further Crime?

Posted on by Imarticus Learning
Reading Time: 3 minutes

AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks. Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend get them to install malware. Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.

How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

If you wish to do ML research, either academic or in the industry, then you need to be a great coder and get to working with the elite in the ML domain. But, for the following reasons, you would still have the advantage.

  1. ML research is the right path since there is an acute shortage of qualified practical doctorates in ML. Spending a few years under the best in the domain of ML can actually help improve your knowledge and practical skills through effective mentorship. There are ML mentors like Geoffrey Hinton, Nando Freitas, Yann LeCun, Andrew Zisserman, Andrew Ng, etc who are well known for their work and contribution to research.
  2. Attaining proficiency in Machine Learning Training needs proficiency in data, mathematics, statistics, linear algebra, calculus, differentiation, integration, and a host of other subjects to do research in the ML domain. If you have these it still takes 3-5 years before you get to writing effective algorithms.

Most software engineering jobs in industries do not provide you time for reading or research. Further, you will lose out on practicing your development skills. Since the ML programs on the market today are more or less ready to use, it makes perfect sense to learn Machine Learning Course.

To answer which way you should proceed read on. One can opt for any of the two ways of applying ML. To research and applications. Let us explore these choices.

ML research:

Learning about the science of machine learning is actual research. An ML researcher is constantly exploring ways to push the scientific boundaries of the science of ML and its applications to the Artificial Intelligence field. Such aspirants do have a post-graduation or even a Ph.D. in CS with frequent and periodical publications of their research presented at the top ML conferences and seminars. They are visible and popular in these research circles. The ML researcher is looking for something to improve upon and thanks to their efforts technology are always cutting edge and progressing in pace with developments.

When you need to tweak your applications and seem to go nowhere with it, it is these ML researchers who can get you up from 95 to 98 percent accuracies or more by offering you a personalized and customized solution. The ML researcher really knows his wares well. The only drawback is that he may never get the opportunity to actually deploy his solutions in applications. He knows the theory and is devoid of practice in SaaS delivery, deploying to production or translating the research finding into a practical app.

Machine Learning application:

In comparison to the researcher, the ML application is about the engineering of ML. An ML engineer will take off from where the researcher left. He is adept at using the research and turning it into a valuable practical application or service. They are adept at services of cloud computing and services like the GCP of Google or AWS from Amazon. They are fluent in Agile practices and can diagnose and troubleshoot anywhere in the SDLC of the product.

These ML engineers are often not as recognized as the ML researcher for want of a decorated Ph.D. and referral citations. But they are the people you must go to if you want your customers to be happy with ML-driven products. These application engineers have years of experience and deployments of thousands of products to their credit.

Consult an ML application engineer before you deploy products or services in the market. Your decisions should be based on your business domain, the product or services on offer and the methods of delivering it to the targeted market.

Expected payouts:

The Gartner report states that by 2020 the domains of ML and AI will generate 2.3 million jobs. Digital Vidya claims the ML career is great since the inexperienced freshmen land jobs that pay 699,807- 891,326 Rs. If your domain expertise is in data analysis and algorithms your salary could be 9 lakh to Rs 1.8 crore Rs pa.

Conclusion:

For most teams/businesses and teams, ML has many apps that are applicable to its specific needs. You do not need to reinvent it but must know how to use it better. Its an awesome tool for the enterprise and customer’s too! Learn ML at Imarticus Learning. Besides learning how to tweak the ML algorithm through hands-on assignments, project work, and workshops you get assured placements, soft-skill, and personality development modules with a resume writing exercise. Hurry and start today!

What Are The Machine Learning Interview Questions?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition.

Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

Here is a collection of a comprehensive set of interview questions about machine learning and guidelines for the answers:

1. What are the different types of machine learning?

Machines learn in the following ways:

Supervised learning: A supervised learning essentially needs a labeled data which are pre-defined data set using which machines provide a result when new data is introduced.

Unsupervised learning: Here machines learn through observation and defines structures through data as these models do not require labeled data.

Reinforcement learning: Here there is an agent and reward which can meet by trial and error method. Machine tries to figure out ways to maximize rewards by taking favorable action.

2. How does machine learning differ from deep learning?

Machine learning essentially uses algorithms to parse data, learn from them and makes informed decisions based on the learnings. Whereas, deep learning structures different algorithms and gimmicks an artificial neural system to make intelligent decisions by learning on its own.

3. Having too many False positives or False negatives which one is better? Explain

It completely depends on the question and domain for which we are figuring out a solution. For a medical domain showing false negatives may prove risky as it may show up no health problems when the patients are actually sick. If spam detection is the domain then false positives may categorize an important email as spam.

4. What is your idea about Google training data for self-driving cars?

Google uses Recaptcha to sense labeled data from storefronts and traffic signals from its eight sensors interpreted by Google’s software. Creator of Google’s self-driving car Sebastian Thrun’s insights is used to build a training data.

5. Your thoughts on data visualization tools and which data visualization libraries do you use?

You may explain your insights data visualization and your preferred tools. Some of the popular tools include R’sggplot, Python’s seaborn, Matplotlib, Plot.ly, and tableau.

6. Explain about a hash table?

In computing, a hash table is a data structure which that implements an associative array. It uses a hash function using which a key is mapped to certain values.

7. Explain the confusion matrix?

Confusion matrix or error matrix essentially visualizes the performance of algorithms in machine learning. In the below table TN= True negative, FN=False negative, TP=True Positive and FP=False positive.

8. Write pseudo-code for a parallel implementation by choosing an algorithm

Enlighten your knowledge about pseudo-code frameworks such as Peril-L and some visualization tools like Web sequence diagram to aid you in showcasing your talent to write a code that reflects parallelism well.

9. How do you handle missing or corrupted data in a dataset efficiently?

You could identify missing or corrupted data in a dataset and ideally drop them or replace them with another value. In pandas isnull() and dropna() are two useful methods which can be used to identify columns of missing or corrupted data and drop them or replace an invalid value with a placeholder value like fillna().

10. Difference between a linked list and an array?

An array consists of an ordered collection of objects wherein it assumes that every object has the same size. A linked list, on the other hand, is a series of objects with directions as to sequentially process them which helps a linked list to grow organically than an array.

Conclusion

For becoming a successful machine learning engineer, you could join Machine learning certification training to make yourself proficient in various topics of machine learning and its algorithms. From this curated list of interview questions, you would have understood that machine learning is an internal part of data science. Use these sample questions to broaden your knowledge about the questions that may pop up in your interview and be ready to spellbind the interviewer with your swift answers.

For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

How Criminals Are Using AI And Exploiting It To Further Crime?

Posted on by Imarticus Learning
Reading Time: 3 minutes

AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks.

Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend to get them to install malware.

Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.

NLP vs NLU- From Understanding A Language To Its Processing!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Today’s world is full of talking assistants and voice alerts for every little task we do. , Conversational interfaces and chatbots have seen wide acceptance in technologies and devices.

Their seamless human-like interactions are driven by two branches of the machine learning (ML) technology underpinning them. They are the NLG- Natural Language Generation and the NLP- Natural Language Processing.

These two languages allow intelligent human-like interactions on the chatbot or smartphone assistant. They aid human intelligence and hone their capabilities to have a conversation with devices that have advanced capabilities in executing tasks like data analytics, artificial intelligence, Deep Learning, and neural networking.

Let us then explore the NLP/NLG processes from understanding a language to its processing.

The differences:

NLP:
NLP is popularly defined as the process by which the computer understands the language used when structured data results from transforming the text input to the computer. In other words, it is the language reading capability of the computer.

NLP thus takes in the input data text, understands it, breaks it down into language it understands, analyses it, finds the needed solution or action to be taken, and responds appropriately in a human language.

NLP includes a complex combination of computer linguistics, data science, and Artificial Intelligence in its processing of understanding and responding to human commands much in the same way that the human brain does while responding to such situations.

NLG:
NLG is the “writing language” of the computer whereby the structured data is transformed into text in the form of an understandable answer in human language.

The NLG uses the basis of ‘data-in’ inhuman text form and ‘data-out’ in the form of reports and narratives which answer and summarize the input data to the NLG software system.

The solutions are most times insights that are data-rich and use form-to-text data produced by the NLG system.

Chatbot Working and languages:

Let us take the example of a chatbot. They follow the same route as the two-way interactions and communications used in human conversations. The main difference is that in reality, you are talking to a machine and the channel of your communication with machines.NLG is a subset of the NLP system.

This is how the chatbot processes the command.

  • A question or message query is asked of the chatbot.
  • The bot uses speech recognition to pick up the query in the human language. They use HMMs-Hidden Markov Models for speech recognition to understand the query.
  • It uses NLP in the machine’s NLP processor to convert the text to commands that are ML codified for its understanding and decision making.
  • The codified data is sent to the ML decision engine where it is processed. The process is broken into tiny parts like understanding the subject, analyzing the data, producing the insights, and then transforming the ML into text information or output as your answer to the query.
  • The bot processes the information data and presents you a question/ query after converting the codified text into the human language.
  • During its analysis, the bot uses various parameters to analyze the question/query based on its inbuilt pre-fed database and outputs the same as an answer or further query to the user.
  • In the entire process, the computer is converting natural language into a language that computer understands and transforming it into processes that answer with human languages, not machine language.

The NLU- Natural Language Understanding is a critical subset of NLP used by the bot to understand the meaning and context of the text form. NLU is used to scour grammar, vocabulary, and such information databases. The algorithms of NLP run on statistical ML as they apply their decision-making rules to the natural-language to decide what was said.

The NLG system leverages and makes effective use of computational linguistics and AI as it translates audible inputs through text-to-speech processing. The NLP system, however, determines the information to be translated while organizing the text-structure of how to achieve this. It then uses grammar rules to say it while the NLG system answers in complete sentences.

A few examples:

Smartphones, digital assistants like Google, Amazon, etc, and chatbots used in customer automated service lines are just a few of NLP applications that are popular. It is also used in online content’s sentiment analysis.NLP has found application in writing white papers, cybersecurity, improved customer satisfaction, the Gmail talk-back apps, and creating narratives using charts, graphs, and company data.

Parting Notes:

NLG and NLP are not completely unrelated. The entire process of writing, reading, and talk-back of most applications use both the inter-related NLG and NLP. Want to learn more about such applications of NLP and NLG? Try the Imarticus Learning courses to get you career-ready in this field. Hurry!

How Can You Learn Deep Learning Quickly?

Posted on by Imarticus Learning
Reading Time: 3 minutes

 

Why is Deep Learning important to learn in today’s world of ever-changing technologies? Human capabilities to do tasks especially on very large volumes of data are limited. AI stepped in to help train computers and other devices to aid our tasks. And how does it do so? The evolved devices use ML to learn by themselves recognizing data patterns and arriving at predictions and forecasts very much like the human brain. Hence one would need to learn all of the above-mentioned concepts to even reach the deep-learning possibility.

In order to learn ML, one would need to have knowledge of Java, R or Python and suites like DL4J, Keras, and TensorFlow among others depending on the areas you are interested in. It is also important to have the Machine Learning Course before one delves into deep-learning. And yes there is a lot of statistics, probability theory, mathematics and algebra involved which you will have to revise and learn to apply.

 

If you are interested in learning Deep Learning quickly, here are the top four ways to do so.

A. Do a course: One of the best ways is to scour the net for the best top free MOOC courses or do a completely paid but skill oriented course. Many are online courses and there are classroom courses as well. For the working professional course from a reputed training partner like Imarticus Learning makes perfect sense. Just remember that to learn Deep learning you will need to have access to the best industry-relevant solutions and resources like mentoring, assured placements, certification and of course practical learning.

B. Use Deep Learning videos: This is a good resource for those with some knowledge of machine learning and can help tweak your performance. Some of the best resources of such videos are ML for Neural Networks by the Toronto University, the tutorials of Stanford University on Deep Learning, ConvNet resources on Github, and videos by Virginia Tech, E and CE, the Youtube, etc.

C. Community Learning: There are communities available online like the Deep Learning community and r-learning communities from Quora, Reddit, etc. Such communities can be of immense help once you have a firm grasp of the subject and need to resolve or are practicing your skills.

D. DIY books: There is a wealth of books available to learn Deep Learning and understand the subject better. Do some research on the best deep-learning resources, the limits of it, differences between ML and deep-learning, and such topics. DIY books are easy to read and hard to practice with. Some excellent books are the TensorFlow-Deep Learning, Nielsen’s Neural Networks-and-Deep Learning, and Chollet’s Python and Deep Learning.

The Disadvantages:

  1. Rote knowledge is never really helpful and the syllabus is very vast and full of complicated subjects.
  2. The practice is the key is only acquired through constantly doing relevant tasks on relevant and industry-standard technology.
  3. Mentorship is very important to learn the current best practices.
  4. Time is a constraint, especially for working professionals.
  5. The best value courses are often paid-for courses.
  6. DIY is bereft of certification and hence a measure of your skills.
  7. The DIY approach may also never train you for the certification exams.
  8. Assured placements in the paid for courses are a huge draw for freshers making a career in deep-learning.
  9. There are non-transferable soft-skills that you require and do not find in the packages.
  10. Industry acceptance is often sadly lacking for the self-learning candidates.

Conclusion:

Learning is always a process where reinforcement and practice scores. Though there are many options available to do deep-learning for free and on one’s own, the route is never easy. Thus it seems the paid courses, like the one at Imarticus Learning, is definitely a better bet. Especially if the course is combined with mentorship of certified trainers, assured placements, widely accepted certification, personalized personality-development modules and a skill-oriented approach with tons of practice as the one at Imarticus is.

The Imarticus Learning courses deliver well-rounded and skilled personnel and offer a variety of latest technology courses which are based on industry demand.

Given the above information, the quickest way to master deep-learning definitely appears to be doing a course at Imarticus. If you want to be job-ready from day one, then don’t wait. Hurry and enroll. We have multiple centers in India – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon and Ahmedabad. So you can consider as per your need!!

 

How Do You Start Learning Artificial Intelligence? Is it Possible to Get Research Work in The Field of AI?

Posted on by Imarticus Learning
Reading Time: 3 minutes

The last decade saw the introduction of Machine Learning Training, Deep-Learning and Neural networks in AI to acquire the capacity to reach computational levels and mimic human intelligence.
The future scope of Machine Learning appears bright with ML enabled AI being irreplaceable and a composite part of evolving technologies in all verticals, industries, production means, robotics, laser uses, self-driven cars and smart mobile devices that have become a part of our lives. It thus makes perfect sense to learn Machine Learning and make a well-paying career in the field. Since the early 50’s a lot of research has gone into making these developments possible, and the trend for continued research into AI has made it the most promising technology of the future.

Why study AI:

AI rules and has become a reality in our lives in so many different ways. From our smartphones and assistants like Siri, Google, Alexa etc, the video games and Google searches we do, self-driven cars, smart traffic lights, automatic parking, robotic production arms, medical aids and devices like the CAT scans and MRI, G-mail and so many more are all AI-enabled data-driven applications, that one sees across verticals and without which our lives would not be so comfortable. Fields like self-learning, ML algorithm creation, data storage in clouds, smart neural networking, and predictive analysis from data analytics are symbiotic. Let us look at how one can get AI skills.
Getting started with AI and ML learning:
To start AI learning the web offers DIY tutorials and resources for beginners and those who wish to do free courses. However, there is a limit to technical knowledge learned in such ‘learn machine learning’ modules, as most of these need hours of practice to get adept and fluent in. So, the best route appears to be in doing a paid classroom Machine Learning Course.

Here’s a simple tutorial to study ML and AI.

1. Select a research topic that interests you:

Do brush through the online tutorials on the topic on the internet. Apply this to small solutions as you practice your learning. If you do not understand the topic well enough use Kaggle the community forum to post your issues and continue learning from the community too. Just stay motivated, focused and dedicated while learning.
2. Look for similar algorithm solutions:
The process of your solution would essentially be to find a fast solution and it helps when you have a similar algorithm. You will need to tweak its performance, make the data trainable for the ML algorithm selected, train the model, check the outcomes, retest and retrain where and when required by evaluating the performance of the solution. Then test and research its capabilities to be true, accurate and produce the best results or outcomes.

3. Use all resources to better the solution:

Use all resources like data cleaning, simple algorithms, testing practices, and creative data analytics to enhance your solution. Often data cleaning and formatting will produce better results than self-taught algorithms for deep learning in a self-taught solution. The idea is to keep it simple and increase ROI.

4. Share and tweak your unique solution:

Feedback and testing in real-time in a community can help you further enhance the solution while offering you some advice on what is wrong and the mentorship to get it right.

5. Continue the process with different issues and solutions:

Make every task step problem you encounter an issue for a unique solution. Keep adding such small solutions to your portfolio and sharing it on Kaggle. You need to study how to translate outcomes and abstract concepts into tiny segmented problems with solutions to get ahead and find ML solutions in AI.

6. Participate in hackathons and Kaggle events:

Such exercises are not for winning but testing your solution-skills using different cross-functional approaches and will also hone your team-performance skills. Practice your collaborative, communicative and contributory skills.

7. Practice and make use of ML in your profession:

Identify your career aims and never miss an opportunity to enroll for classroom sessions, webinars, internships, community learning, etc.
Concluding notes:
AI is a combination of topics and research opportunities abound when you learn to use your knowledge professionally. Thus the future scope of Machine Learning which underlies AI contains newer adaptations which will emerge. With more data and emerging technological changes, the field of AI offers tremendous developmental scope and employability in research and application fields to millions of career aspirants.
Do a machine learning training at Imarticus Learning to help with improving your ML practical skills, enhance your resume and portfolio and get a highly-paid career with assured placements. Why wait?