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Tag: Machine Learning

AI and Food: Safer and More Tasty Food?

Posted on by Imarticus Learning
Reading Time: 4 minutes

 

In February 2019, Tristan Greene wrote an article in The Next Web and quoted an IBM research study that suggested that artificial intelligence could improve the taste of food by creating new hybrid flavors. It took a part of the Internet by storm, less for its clickbait headline and more for its actuality. Greene was writing facts when he began his article with this: “AI will soon decide what we eat”.

Let’s explore the what, the why, and the how. We are sure you already know the why so we’ll mostly skip it.

Artificial Intelligence + Food. Really?

That seems to be a sensible question but not a surprising one. AI and machine learning have already taken over the world with them influencing everything from blockchain to computer vision to chemistry. So why not food production?

Now IBM, other tech giants, and new startups are changing that by feeding AI systems millions of different types of data in the areas of sensory science, consumer preference and flavor palettes to help generate new or advanced flavours that can literally put your mouth on fire. Or make it drool all day. Or make even the most tasteless food taste like heaven. Kale and quinoa, anyone?

The food industry has already scrambled to use artificial intelligence and machine learning for its sake. Take, for example, the world’s first automatic flatbread-making robot called Rotimatic which limits user control to just putting the ingredients into the appliance. It does all the dirty work by itself and claims to bake hot flatbread in under a minute.

Not just kitchen appliances, the food that we eat and its ingredients are also being influenced by AI and other techniques even as we debate whether genetically modified food products are safe for human consumption. Researches involving changes in the cooking style, omission or replacement of certain ingredients, and others have all been suggested by AI-driven tools. While none of them have hit the shelves yet, this new tool by IBM looks like it’s just around the corner.

According to the study, IBM and a company pioneering in flavors and food innovation named McCormick & Company created a novel AI system whose aim is to create new flavours. Published in February 2019, the blog post promised that some of its findings will be available on the shelf by the end of the year. While it is September and we still wait, let’s have a look at the scope of AI in the food industry.

How Does AI Help Food Become Better?

To answer this question, Greene uses the analogy of Google Analytics tools. Publicly available data like recipes, menus, and social media content about these recipes along with trends in the food industry are fed to AI systems. These then generate fresh, actionable insights.

An example is a tool that can show restaurants what the most popular food will be every month for the next 12 months. If this is a possible scenario, the restaurant can prepare itself and maybe even surprise its customers into submission, eventually becoming popular and running a successful service.

The same goes for farming models where new techniques are needed to plant and grow more produce as the population gets out of the window due to lack of space. Everyone involved in researches dealing with AI and the food industry is positive about what can be done.

Existing data is of prime importance if such tools are to bear any results. In the above example involving IBM, the tool is able to create new flavors because of the existence of data on different flavours that we currently have. In a way, AI is only helping us discover flavors sooner.

AI Everywhere in the Food Industry

Till now, we spoke about the use of AI in farming, food recipes, and restaurants. But what about food processing? Media suggests that AI is everywhere – from its help in sorting foods to making supermarkets more super.

According to a Food Industry Executive, there are a lot of examples that highlight the significance of AI in the food industry. Some of them are listed below, thanks to Krista Garver:

  • Food sorting – AI helps understand which potatoes (by their size and quality and age) should be made into French fries and which ones are suitable for hash browns or potato chips or some other food. This involves the usage of cameras and near-infrared sensors to study the geometry and quality of fruits and vegetables
  • Supply chain management – This is obvious: food monitoring, pricing and inventory management, and product tracking (from farms to supermarkets)
  • Hygiene – AI can detect if workers are wearing all the necessary equipment. Since AI tools are fed data about what constitutes 100% hygiene, they can constantly check the attire of workers and rate them on the basis of their current clothing. Is a worker not wearing a plastic hat? An alert goes to his manager
  • New products – This is similar to the IBM example seen above. Predictive algorithms can be used to understand what flavors are most popular in people of certain age groups. Why do kids love Kinder Joy? What is or are the ingredients that make them go bonkers?
  • Cleaning – This is the most promising one where ultrasonic sensing and optical fluorescence imaging can be used to detect bacteria in a utensil; this information can then be used to create a customized cleaning process for a batch of similar utensils.

Conclusion

It is mind-numbing (mouth-watering, too?) to visualize these products actually coming into form in a few years. Which is why there is no doubt that AI will revolutionize the food market. The only question that then remains: has the revolution already begun now that you can’t say no to a bunch of addictive products?

Artificial Intelligence as an Anti-Corruption Tool

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Reading Time: 4 minutes

Artificial Intelligence as an Anti-Corruption Tool

It is obvious why anyone would want to put a cork on corruption and then throw it into space to disintegrate itself. So, when a group of scientists from the University of Valladolid in Spain put together a computer model that can predict instances of graft in government agencies, the whole world took notice.

Here are some of the top takeaways from the study that was published in FECYT – Spanish Foundation for Science and Technology. It was first published online in Springer on 22 November 2017.

What was the study about?

According to the research paper published in Springer, the study created a computer model based on neural networks that would send out warnings for possible instances of graft occurring in a government office. These warnings can then be used for corrective and preventive measures, which in other words, means changing the way a government functions or weeding out certain bad apples, for lack of a better term.

The model in the study uses corruption data extracted from several provinces in Spain where graft occurred between 2000 and 2012. A lot of different factors were at play that defined how a routine incident of corruption would occur in any given government agency.

The researchers’ aim was to understand what factors play a key role and then administering changes to those factors in an attempt to eradicate corruption. Of course, this process will be iterative, as no “bad habit” can be weeded out in a single go.

What were the findings?

According to the paper published by the researchers, the following are the key takeaways. Since Spain follows a customized form of parliamentary monarchy, it can be easily translated and adapted for other similar governments. Of course, the data will need to be updated.

  • Public corruption is a cause of multiple factors such as:
    • Taxation of real estate and a steady increase in property prices
    • Nature of economic growth, GDP rate, and inflation
    • Increase in the total number of non-financial institutions
    • Sustenance of any single political party for a “long time”
  • Since data on actual cases of corruption was used to create the model, it provides for a better look at the factors compared to how the model would have been had it depended solely on the perception of corruption. Such studies have not yielded much either have they garnered any interest from the public
  • Corruption can be predicted three years before they are bound to happen

Where does AI come in the picture?

Since all of this sounds too good to be true, it is wise to ask what the role of artificial intelligence is in this study. In order to do that, let us go back to other studies that have tried to predict corruption.

All of the previous studies on the subject have depended on data that were more or less subjective indexes of perception of corruption, reports Science Daily. What this means is that the only type of data being used is something that is available on the public domain. Since the government can sometimes come in between the sourcing of this data by private agencies like Transparency International, the database stops becoming useful. All it will give the model is data that does not reflect the true gravity of the situation.

On the other hand, when actual data is used, there is much for artificial intelligence to feed itself and then bring out a model that can be used to predict the very nature of corruption. This is where this new study excels when compared with historical reports.

The biggest role of artificial intelligence in this exercise is to find a correlation in a set of data through a process that attempts to mimic the human brain functionality. If we feed human being scores of detailed court cases about corruption charges on government employees, he will take decades to dig through them and still end up without an actionable conclusion.

A neural network, on the other hand, analyses the data, studies its various factors, and creates a relationship between them to see what connects with what. Let’s take a rough example to make this clear.

If out of 10 cases of corruption, 8 of them involves a particular modus operandi and a similar cause for the exchange of money to take place, then such a connectionist system will flag that as a recurring factor. This information, along with several others, is then used to reach a conclusion. Of course, this example is an imaginary one, and the amount of data fed in the study were a lot higher, which makes the result even more constructive. Over 12 years of data of actual corruption cases are bound to give such an AI tool enough ground datum to work with. But, it should still be noted that no amount of data is sufficient when one is trying to execute the predictive analysis.

Finally, according to the Chr. Michelsen Institute, this type of predictive tool that depends on patterns can well be a smart anti-corruption tool. Its ability to handle big data and detect anomalies in it is what makes it a promising new system that could be adopted by late the 2020s. However, it also points out the single biggest concern over its use: it will force for more surveillance in the world as data about even the smallest corruption cases will be added into the system. Data that will include personal details of individuals.

Conclusion

It is a great relief that AI in theory at least does not mimic the dangers portrayed by science-fiction films and is being seen as a technology that can help humanity lead a better life. Its usage in anti-corruption neutral networks is a step in the right direction, and with added research, it will pave way for better governance. Where those being governed have one less accusation to make against the government.

How AI in The Energy Sector Can Help to Solve The Climate Crisis?

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Reading Time: 3 minutes

How AI in the Energy Sector Can Help Solve the Climate Crisis

Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackle the climate crisis of all times.

The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.

Here is the list of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are

AI helps compile and process data:

We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.

AI can help reduce consumption of energy by ‘server farms’

The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.
The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.
The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.
The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.
Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.

Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.

If you care enough to be a part of this pressing solution to environmental change, learn at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.

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.

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!!

 

AI is Now Being Used in Beer Brewing!

Posted on by Imarticus Learning
Reading Time: 3 minutes

AI is now being used in beer brewing -from creating unique beer recipes to adapting recipes as per customer feedback. AI is doing it all…

With the advent of the digital revolution, Artificial Intelligence (AI) has gained immense impetus in recent years. Today, everyone is connected to everything because of the growing importance of the Internet of Things. Right from the time, you wake up until the time you close your day, technology plays a key role in taking you forward.

Alexa and Siri have now become household names and no doubt, why “Her” was a blockbuster in the cinemas. AI and Machine Learning are here to make your work easier, and your life smoother. It is also brilliant to know how even breweries today are using AI to enhance their beer production.

Brewed with AI
As discussed earlier, digitization and technology have significantly impacted our lives across spectrums, and there are several examples of various companies that have started employing AI in their processes to serve their customers better. Breweries are nowhere behind in this race of digitization, so let us discuss a few examples of how they are using AI in order to enhance the experience of the consumers.

Intelligent X
Intelligent X is one of the best examples of how a platform employed AI to enhance their beer. It came up with the world’s first beer, which is brewed with Artificial Intelligence Course and advances itself progressively based on customer feedback. They use AI algorithms and machine learning to augment the recipe and adjust it in accordance with the preferences of the customers. The brewery offers four types of beer for the customers to choose from:

  • Black AI
  • Golden AI
  • Pale AI
  • Amber AI

In order to brew the perfect beer that pleases all your senses, all you need to do is sign up with IntelligentX, train their algorithm according to what appeals to your palate, and you are good to go. In addition to this, you can follow the URL link on your beer can and give your feedback so that they can create a beer you would like. These beers come in classy and minimally designed black cans that reflect their origin and give a feeling that what you are experiencing is the beer from the future.

Champion Brewing
Another example of a very intelligent deployment of AI in brewing beer is that of Champion Brewing. They used machine learning in the process of developing the perfect IPA. They took the big step by initially getting information regarding the best and the worst IPA selling companies to get an insight into how to go about the entire project. Based on the same, did they determine the algorithm of brewing the best IPA with their AI?

RoboBEER
An Australian research team found out that the form of a freshly poured beer affects how people enjoy it. Building on to this, they created RoboBEER, which is a robot that can pour a beer with such precision that can produce consistent foam, pour after pour. These researchers also made a video of how the RoboBEER poured the beer tracked the beer color, consistency, bubble size, and all the other attributes. They then showed the same videos to everyone who participated in the research in order to get seek their feedback and thoughts with regard to the beer’s quality along with its clarity.
Conclusively, this shows how AI has become the nascent yet a very preferred trend, which is even being followed by the breweries around the world. It has added an unusual turn to the way the perfectly brewed well-crafted beer makes its way to your glass. With the help of this ever-evolving technology, we can anticipate our favorite drinks to be made precisely in accordance with our preference only with the help of your smartphone.

By deriving minutest of the insights right from the foam of the beer till the yeast used in the same, companies these days are striving to deliver their best with the help of immense research and execution from the ideation derived from their research amalgamating it with AI and Machine Learning. Looking at the various examples, we can surely say that we are living in the future in the present.

For more information you can also visit – Imarticus Learning contact us through the Live Chat Support or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi and Gurgaon.

How can AI be integrated into blockchain?

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Reading Time: 2 minutes

Blockchain technology has created waves in the world of IT and fintech. The technology has a number of uses and can be implemented into various fields. The introduction of Artificial Intelligence Training (AI) makes blockchain even more interesting, opening many more opportunities. Blockchain offers solutions for the exchange of value integrated data without the need for any intermediaries. AI, on the other hand, functions on algorithms to create data without any human involvement.
Integrating AI into blockchain may help a number of businesses and stakeholders. Read on to know more about probable situations where AI integrated blockchain can be useful.
Creating More Responsive Business Data Models
Data systems are currently not open, and sharing is a great issue without compromising privacy and security. Fraudulent data is also another issue which makes it difficult for people to share data. Ai based analytics and data mining models can be used for getting data from a number of key players. The use of the data, in turn, would be defined in the blockchain records, or ledger. This will help data owners maintain the credibility, as the whole record of the data will be recorded.
AI systems can then explore the different data sets and study the patterns and behaviors of the different stakeholders. This will help to bring out insights which may have been missed till now. This will help systems respond better to what the stakeholder wants, and guess what is best for a potentially difficult scenario.
Creating useful models to serve consumers
AI can effectively mine through a huge dataset and create newer scenarios and discover patterns based on data behavior. Blockchain helps to effectively remove bugs and fraudulent data sets. New classifiers and patterns created by AI can be verified on a decentralized blockchain infrastructure, and verify their authenticity. This can be used in any consumer-facing business, such as retail transactions. Data acquired from the customers through blockchain infrastructure can be used to create marketing automation through AI.
Engagement channels such as social media and specific ad campaigns can also be used to get important data-led information and fed into intelligent business systems. This will eventually help the business cycle, and eventually improve product sales. Consumers will get access to their desired products easily. This will eventually help the business in positive publicity and improve returns on investments (ROI).
Digital Intellectual Property Rights
AI enabled data has recently become extremely popular. The versatility of the different data models is a great case study. However, due to infringement of copyrights and privacy, these data sets are not easily accessible. Data models can be used to show different architectures that cannot be identified by the original creators.
This can be solved through the integration of blockchain into the data sets. It will help creators share the data without losing the exclusive rights and patents to the data. Cryptographic digital signatures can be integrated into a global registry to maintain the data. Analysis of the data can be used to understand important trends and behaviors and get powerful insights which can be monetized into different streams. All of this can happen without compromising the original data or the integrity of the creators of the data.

How do you balance Machine Learning theory and practice?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Machine learning is no longer a technology from the future. The technology giants like Google, Facebook, Netflix, etc. have been using machine learning to improve their user experience for a very long time. Now, the applications of machine learning are growing across the industries and this technology is driving businesses worth billions of dollars. Along with the applications,  the demand for professionals with expertise in ML has grown immensely in the past few years.
So, it is indeed a good time to learn machine learning for better career prospects. A machine learning course is the best practical way to start your learning process. However, often people get too much stuck to the theory and fall behind in the practical experience. Well, it is not the best way to learn anything. This article will help you balance learning machine learning theory and practice. Read on to find out more.
Theory vs Practice 
For practitioners of ML, the theory and practice are complementary aspects of their career. To become successful in this field, you will have to strike the balance between what you read and the problems in real life. So many people avoid building things because it is hard. Building involves constant tracing of bugs, endlessly traversing stack overflow, attempts to bring so many parts together and so many more work. Theory on the other hands is comparatively easy.
You can find all the concepts settled in place and we can just consume everything as to how we wish things will work. But if it doesn’t feel hard, you are not learning anything properly. It will be a lot easier for us to rip through journals and understand the concepts, but reading about the achievements of others will not make you any better in this field. You have to build what you read and fail so many times to get an understanding that cannot be achieved by reading alone.
Build what you read
It is the one simple thing you can do to strike a balance between theory and practice. Build a neural network. It may perform poorly, but you will learn how different it is from the journals. Attend a Kaggle competition and let your ranking stare at you even if it is low. Hack together a javascript application to run your ML algorithms in the back end only just to see it fail for unknown reasons.
Always do projects. Your machine learning certification program might have projects as part of their curriculum, but don’t be limited to those. Just remember that everything you make during the learning process does not have to work. Even the failures are great teachers in this process. They will provide you with the practical experience you will need to excel in the industry.
Practicing everything you read may make it harder for you, but once you learn to volley theory and practice back and forth, you will certainly get the results better than you were looking for. Only such a balanced approach towards ML will help you make an effect on the real world problems.

How Machine Learning is Reshaping Location-Based Services?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Today life is a lot different from what it used to be a decade ago. The use of smartphones and location-empowered services is commonplace today. Think about the driving maps, forecasts of local weather and how the products that flash on your screen are perhaps just what you were looking for.
Location-enabled GPS services, devices that use them and each time we interact and use them generates data that allows data analysts to learn about our user-preferences, opportunities for expansion of their products, competitor services and much more. And all this was made possible by intelligent use of AI and ML concepts.
Here are some scenarios where AI and ML are set to make our lives better through location-based services.

  • Smart real-time gaming options without geographical boundaries.
  • Automatic driver-less transport.
  • Use of futuristic smartphone-like cyborgs.
  • Executing perilous tasks like bomb-disposals, precision cutting, and welding, etc.
  • Thermostats and smart grids for energy distribution to mitigate damage to our environment.
  • Robots and elderly care improvements.
  • Healthcare and diagnosis of diseases like cancer, diabetes, and more.
  • Monitoring banking, credit card and financial frauds.
  • Personalized tools for the digital media experience.
  • Customized investment reports and advice.
  • Improved logistics and systems for distribution.
  • Smart homes.
  • Integration of face and voice integration, biometrics and security into smart apps.

So how can machine learning actually impact the geo-location empowered services?
Navigational ease:
Firstly, through navigation that is empowering, democratic, accurate and proactive. This does mean that those days of paper maps, searching for the nearest petrol station or location, being late at the office since the traffic pileups were huge and so many more small inconveniences will be a thing of the past. We will gracefully move to enhanced machine learning smartphones that use the past data and recognize patterns to inform us if the route we use to commute to office has traffic snarls and provide us with alternative routes, suggest the nearest restaurant at lunchtime, find our misplaced keys, help us locate old friends in the area etc all by using a voice command to the digital assistant like Alexa, Siri or Google.
ML can make planning your day, how and when to get to where you need to be, providing you driving and navigational routes and information, and pinging you on when to leave your location a breeze. No wonder then that most companies like Uber, Nokia, Tesla, Lyft and even smarter startups that are yet to shine are investing heavily on ML and its development for real-time, locational navigational aids, smart cars, driverless electric vehicles and more.
Better applications: 
Secondly, our apps are set to get smarter by the moment. At the moment most smartphones including Google, Apple, Nokia among many others are functioning as assistants and have replaced those to-do lists and calendar keeping for chores that include shopping, grocery pickups, and such.
Greater use of smart recommendatory technology:
And thirdly, mobile apps set smartphones apart and the more intelligent apps the better the phone experience gets.  The time is not far off when ML will be able to use your data to actually know your preferences and needs. Imagine your phone keeping very accurate track of your grocery lists, where you buy them, planning and scheduling your shopping trips, reminding you when your gas is low, providing you with the easiest time-saving route to commute to wherever you need to go and yes, keep dreaming and letting the manufacturer’s know your needs for the future apps. The smart apps of the future would use your voice commands to suggest hotels, holiday destinations, diners, and even help you in budgeting. That’s where the applications of the future are headed to.
In summation, ML has the potential to pair with location-using technologies to improve and get smarter by the day. The future appears to be one where this pairing will be gainfully used and pay huge dividends in making life more easily livable.
To do the best machine learning courses try Imarticus Learning. They have an excellent track record of being industrially relevant, have an assured placement program and use futuristic and modern practical learning enabled ways of teaching even complex subjects like AI, ML and many more. Go ahead and empower yourself with such a course if you believe in a bright locational enabled ML smart future.