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Tag: Artificial Intelligence

Understand the Difference: Artificial Intelligence Vs Machine Learning

Posted on by Imarticus Learning
Reading Time: 3 minutes

Artificial Intelligence and Computer Sciences, data sciences and nearly everyone today uses the terms Machine Learning/ML and AI/ Artificial Intelligence interchangeable when both are very important topics in a Data Science Course. We need to be able to differentiate the basic functions of these two terms before we do a data science tutorial where both ML and AI are used on another factor namely data itself.
AI is not a stand-alone system in the data science tutorial. It is a part of the programming that artificially induces intelligence in devices and non-humans to make them assist humans with what is now called the ‘smart’ capability. Some interesting examples of AI we see in daily life are chatbots, simple lift-arms in warehousing, smart traffic lights, voice-assistants like Google, Alexa, etc.
ML is about training the machine through algorithms and programming to enable them to use large data volumes, spot the patterns, learn from it and even write its own self-taught algorithms. This experiential learning is being used to produce some wonderful machines in detecting cancers and brain tumours non-invasively, spot trends and patterns, give recommendations, poll trends, automated driverless cars, foresight into possibilities of machine failure, tracking vehicles in real-time, etc. It is best learned at a formal Data science Course.

Difference Between Machine Learning And Artificial Intelligence

Here are the basic differences between ML and AI in very simple language.

  • ML is about how the machine uses the algorithm to learn. AI is the ability of machines to intelligently use the acquired knowledge.
  • AI’s options are geared to succeed. ML looks for the most accurate solution.
  • AI enables machines through programming to become smart devices while ML relates to data and the learning from data itself.
  • The solutions in AI are decision-based. Ml allows machines to learn.
  • ML is task and accuracy related where the machine learns from data to give the best solution to a task. AI, on the other hand, is about the machine mimicking the human brain and its behavior in resolving problems.
  • AI chooses the best solution through reasoning. ML has only one solution with which the machine creates self-learned algorithms and improves accuracy in performing the specific task.

Both AI and ML exist with the very life-breath of data. The interconnection is explained best through ‘smart’ machines to do such human-tasks through ML algorithms to scour and enable the final inferential steps of gainful data use. AI and ML are both essential to handle data which can run into a variety of complex issues in managing data. ML is the data science tutorial way you would train, imbibe and enable the computers and devices to learn from data and do all jobs using algorithms. Whereas AI itself refers to using machines to do the tasks which are in data-terms far beyond human computing capabilities. And in short, the data scientist/analyst is the one person who uses both AI and ML in his career to effectively use data and tools from both AI and ML suites.
One does not need a technical degree to choose the umbrella career of data science which teaches you both AI and ML. However, it is a must that you get the technical expertise and certification which is a validation of being job prepared from a reputed institute like Imarticus by doing their Data science Course. You will need an eclectic mix of personal traits, technologically sound knowledge of AI, ML, programming languages and a data science tutorial to set you on the right track. Hurry!
Conclusion:
The modern day trend of using data which is now an asset to most organizations and daily life can be put to various applications that can make figuring out complex data and life simpler by using AI achieved through ML programming.
The Data science Course at Imarticus Learning turns out sought-after trained experts who are paid very handsomely and never suffer from want of job-demand. Data grows and does so every moment. Do the data science tutorial to emerge career-ready in data analytics with a base that makes you a bit of a computer and databases scientist, math expert and trend spotter with the technical expertise to handle large volumes of data from different sources, clean it, and draw complex inferences from it.

What are the best practices for training machine learning models?

Posted on by Imarticus Learning
Reading Time: 3 minutes

As we all know, Machine learning is a popular way of learning at your own pace. Machine learning also facilitates learning based on your likes and interests. For example, you are a person who is interested in space and astronomy, a machine learning driven course to learn mathematics for you, will first ask you few basic questions about your interest.

Once it establishes your interest, it will give examples of mathematical calculations using objects of space to keep you engaged. So, how are these machines able to establish your interest? What are the best practices for training machine learning models is something that we will see in this article.

Machine learning is based on three important basics.
• Model: A Model is responsible for identifying relationship between variables and to make logical conclusion.
• Parameters: Parameters are the input information that is given to Model to make logical decisions.
• Learner: Learner is responsible for comparing all the Parameters given and deriving the conclusion for a given scenario.

Using these three modules, machine is trained to handle and process different information. But it is not always easy to train the machine. We need to adopt best practices for training machines for accurate predictions.

• Right Metrics: Always start the machine learning training or practice with a problem. We need to establish success metrics and prepare a path to execute them. This is possible when we ensure that the success metrics that have been established are the right ones.

• Gathering Training Data: The quality and quantity of data used is of utmost importance. The training data should include all possible parameters to avoid misclassifications. Insufficient data might lead to miscalculated results. The quantity of data also matters. Exposing the algorithms to a small set of humongous data can make them responsive to a specific kind of information again leading to inaccurate results when exposed to something other than the test data.

• Negative sampling: It is very important to understand what is categorized as negative sampling. For example, if you are training your data for a Binary classification model, include data that requires other models like multi class classification model. By this, you can train the Machine to handle negative sampling too.

• Take the algorithm to the database: We usually take the data out from the database and run the algorithm. This takes lot of effort and time. A good practice would be to run the training algorithm on the database and train it for the desired output. When we run the equation through the kernel instead of exporting the data, we not only save hours of time but we also prevent duplication of data.

• Do not drop Data: We always create pipelines by copying an existing pipeline. But what happens in the background is, the old data gets dropped many a times to provide place for the fresh data. This can lead to incorrect sampling. Data dropping should be effectively handled.

• Repetition is the key: The Learner is capable of making very minute adjustments for refining the model to obtain the desired output. To achieve this, the training cycle must be repeated again and again until the desired Model is obtained.

• Test your data before actual launch: Once the Model is ready test the data in a separate test environment till you obtain the desired results. If your data sample is all the data up to a particular date for which you have all predictions, the test should be conducted on upcoming data to test the predictions.

Finally, it is also important to review the specifications of the Model from time to time to test the validity of the sample. You may have to upgrade it after a considerable amount of time depending on the type of model.

There is a lot to learn about ML(Machine Learning) that cannot be explained in a simple article like this. The Machine learning future in India is very bright. If you have the desired machine learning skills and need to pursue big data and machine learning courses in India, learn from pioneers like Imarticus.

How AI is Helping the Financial Sector Cover Regulatory and Compliance?

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Reading Time: 2 minutes

Synopsis

Artificial intelligence (AI) is here and is making waves in the financial industry. From sales management to compliance and protection against cybercrime, here is everything you need to know about AI 

On any given day, you as a consumer can carry out transactions online without having to worry about security and if your payment goes through or not. How is this possible?  From shopping online to overseas transfer, emerging tech such as Artificial Intelligence, Blockchain, Cloud has revolutionized the way the Financial industry works. In the past decade, Fintech has seen new dawn with many organizations heavily investing in Artificial Intelligence.

So, what is Artificial Intelligence? Simply put Artificial Intelligence courses are the ability of a machine to learn and process data for insights that impact the business.

It means that a machine is capable of learning on its own and arriving at solutions that can reduce cost and improve the efficiency of any business. In the financial sector, artificial intelligence is involved in every component today. From regulatory compliance to consumer insights, AI is changing the way the Fintech industry functions.

One of the most important aspects of the financial industry is regulatory compliance and cybersecurity. Another facet of this is sales management. As there is a shift in the way things work, it is important for the leaders of organizations to take stock of the benefits and consequences of deploying AI in their company.

Here are the top things that one must be prudent of while hailing in this new technology

Regulatory Compliance

Before Artificial Intelligence, the burden of compliance and authority rested with individuals and professionals who were trained in the field.  This also accounted for human errors, incorrect processing of data, and took a longer
duration of time. With AI, there is minimal human intervention when it comes to regulatory compliance and the machine also takes less time when it comes to analyzing the right data and arriving at a solution.

This will also impact the business drastically and reduce costs. In the financial sector, compliance is something that cannot be compromised on, and thereby use of AI will have a positive impact.

Top Features of Amazon Sagemaker AI Service

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Amazon Sagemaker is the latest service that has changed the programming world and provided numerous benefits to machine learning and AI. Here’s how:

The Amazon Sagemaker or the AWS as its popularly known as has many benefits to organisations. It can scale large amounts of data in a short span of time, thereby reducing the overall cost of data maintenance.  Amazon Sagemaker provides data scientists with the right data to make independent strategic decisions without human intervention. It helps to prepare and label data, pick up an algorithm, train an algorithm and optimise it for deployment. All this is achieved at a significantly low cost.

The tool was designed to ensure that companies have minimum issues while scaling up when it comes to machine learning.  The most common programming language used for AI programs Python and also Jupyter Notebook is in-built into the Amazon Sagemaker.

You can start by hosting all your data on Amazon Sagemaker’s Jupyter Notebook and then allow it to process that information, post which the machine will begin the learning process.

One of the best features of Amazon Sagemaker is the ability to deploy a model which can be a tricky business. Apart from this, we have listed down the top features of Amazon Sagemaker below.

Build the Algorithm

The Sagemaker allows organisations to build accurate and relevant data sets in less time by using algorithms that support artificial intelligence and machine learning courses.  It becomes extremely easy to train machines using this service as they are given easy access to relevant data sources in order to arrive at correct decisions. It has the ability to automatically configure frameworks such as  Apache, SparkML, TensorFlow and more thereby making it easier to scale up.

Testing can be done locally

When there are broad open source frameworks such as Tensorflow and Apache MXNet, it becomes easy to download the right environment and locally test the prototype of what you have built. This reduces cost significantly and does not remove the machine from the environment it is supposed to function in.

Training

Training on Amazon Sage Maker is easy as the instructions for the same are specific and clear. Amazon SageMaker provides end to end solution to the training that is there is a setup of computer distributed cluster, and then the training occurs and when results are generated the cluster is torn down.

Deployment

Amazon Sagemaker has the feature of deploying on one click once the model production is complete and the testing is done.  It also has the capacity to do A/B testing to help you test the best version of the model, before deploying it. This ensures that you have the best results for the program itself.  This will have a direct impact on reduced cost due to continuous testing and monitoring.

Conclusion

Amazon Sagemaker service provides many benefits to companies who are heavily invested in deep learning and AI. These enable data scientists to extract useful data and provide business insights to organisations.

Technical Approaches for building conversational APIs

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Today’s GUIs can understand human speech and writing commands like the Amazon Echo and Google Home. Speech detection and analysis of human sentiments are now being used in your daily life and on your smart devices like the phones, security systems and much more. This means learning the AI approach.

The six smart system methods:
The existing artificial intelligence process and systems are not learning-based on interactive conversations, grounded in reality or generative methodology. The system of AI training needs to be one of the following.

Rule-based systems can be trained to recognize keywords and preset rules which govern their responses. One does not need to learn an array of new commands. It does need trained workforce with domain expertise to get the ball rolling.

Systems that are based on data retrieval are being used in most applications today. However, with speech recognition and conversational Artificial Intelligence courses being buzzwords, the need to scale and update quickly across various languages, sentiments, domains, and abilities needs urgent skilled manpower to update and use knowledge databases which are growing in size and volume.

The Generative methodology can overcome the drawbacks of the previous methods. In simple language, this means that the language system could be trained to generate its own dialogues rather than rely on pre-set dialogues.
The popular generative and interactive systems today incorporate one or all of the following methods to train software.

• Supervised learning is used to develop a sequence-to-sequence conversation mapping customer input to responses that are computer-generated.

• Augmented learning addresses the above issues and allows optimization for resolution, rewards, and engaging human interest.

• Adversarial learning improves the output of neural dialog which use testing and discriminatory networks to judge the output. The ideal training should involve productive conversations and overcome choice of words, indiscriminate usage and limitations on prejudging human behavior.

Methods relying on the ensemble that use the method most convenient to the context are being used in chatbots like Alexa. Low dialogue levels and task interpretation are primarily addressed. This method though cannot provide for intelligent conversations like human beings produce.

Learning that is grounded uses external knowledge and context in recognizing speech patterns and suggesting options. However, since human knowledge is basically in sets of data that is unstructured, the chatbots find it difficult to make responses of such unstructured data that are not linked to text, images or forms recognized by the computer.

The use of networking neural architecture into smaller concept based parts and separating a single task into many such components instantly while learning and training can help situational customization, external memory manipulation and integration with knowledge graphs can produce scalable, data-driven models in neural networks.

Learning interactively is based on language. Language is always developing and interactive when being used to enable collaborative conversations. The operator has a set goal based on the computer’s control and decisions. However, the computer with control over decision making cannot understand the language. Humans can now use SHRDLURN to train and teach the computer with consistent and clear command instructions. Based on experience it was found that creative environments were required for evolving models.

Which method to use is and how is where the creativity of human operators counts! To learn machine learning or an artificial intelligence and the systems of deploying it is the need of the hour no matter which technical method you use.

Is Machine Learning Right for You?

Posted on by Imarticus Learning
Reading Time: 2 minutes

The world today has been technologically changed by machine learning and big data analytics. Our challenges today, lie in understanding the large volumes of data we have created and using it intelligently. 

That is precisely what Machine Learning, Artificial Intelligence and machine learning courses in India have helped us with.Examples are everywhere and especially on your smartphone. ML has helped understand your shopping preferences and auto-suggests what you could be interested in. The same thing happens when you use your Facebook account which tags your friends and suggests videos that may interest you.

The Data Analyst and ML Engineer Roles
As a Data Analyst, your end goal is to use data to produce insights that are actionable by other humans. The ML Engineer does the same. However, its end goal is used by artificial intelligence systems to make the machines or systems behave in a particular way. This decision will impact the service or product and eventually the success of the enterprise.

Skills Required
ML requires a mix of skills to understand the complete environment, the how and the why of the issues you are designing and dealing with. Machine learning courses should ideally cover

Computer Science and Programming
Fundamentals including data structures, algorithms with their functioning, complex and complete solutions, approximation in algorithms, and system architecture. Hackathons, competitions in coding and plenty of practice are best at honing skills.

Statistics and Probability
The engine for ML runs on these and helps it in validating and building models from the provided algorithm which evolves from statistical models.

Evaluation and Data Modeling
These are important as ML build the model based on measures, weights, models, iterative algorithms and strategies it develops depending on its learning from the base algorithm.

Applying Libraries and ML Algorithms
Libraries and APIs like Theano, Scikit-learn, Tensor Flow etc., need a precise model and effective application for success.

Software Engineering and System Design
Output depends on the software and its design for applicability to provide robust, scalable and efficient solutions.

Job Roles with Demand
Data analysts, core ML engineers, applied ML engineers, and ML software engineers are jobs that will exponentially rise. Skills and Big data Hadoop training courses that help in applying ML algorithms and libraries will stand you in good stead. System design and software related jobs using ML, data modeling and evaluation, ML probability and statistics experts, and CS fundamentals and programming specialists jobs offer huge potential for professional development in the near future.

The Future of Machine Learning
Machine Learning, data analytics, AI and predictive analysis has no limits to its applicability and has already impacted every field like health, computers, life sciences, banking, education, insurance, finance, and literally every field you can think of.

Your weather forecasts, prices on stock exchanges, trends for the next decade, oil exploration, the MRI machines, predicting the subsequent breakdown, strategy building for marketing, automatic machine lines, and production are all today complex uses of techniques of using machine learning and AI for data analysis, analytics and predictive analysis. Will there be any field that is not impacted then by ML in the future?

If ML interests you then now is the time to update your knowledge and upgrade your skill-sets. There are courses and materials readily available. However, you will need a plan of action that you must adhere to. Good Luck!

A Beginner’s Guide- ‘Books for Learning Artificial Intelligence’

Posted on by Imarticus Learning

Reading Time: 2 minutesData collections are readily available with most enterprises. However, one has to learn how to program with artificial intelligence systems like the computer to be able to understand the data and use the computer to get it to assimilate the data, learn from it and present the data after its due analysis.

How to do an AI course?

This process of AI, data analytics, machine learning and predictive forecasts based on the analysis is what most machine learning and artificial intelligence courses teach.
There are many books and free materials in the form of books that one can read and learn from to understand these concepts. One can do the course in virtual classrooms, one-to-one learning or even practice after reading online.
Some of the best books to learn AI are:
Thomas Laville’s Deep Learning for beginners and Artificial Intelligence by the same author, Malcolm Frank and others titled “When machines do everything”, James Barrat’s Our Final Invention, Michael Taylor’s Neural Networks, and many others like Grokking Algorithms, Introduction to Machine Learning with Python, and Python Machine Learning by example which are sold on Amazon.

How to do an ML course?

Machine learning courses incorporate the learning of neural systems, characterization trees, vector machines bolstering and boosting techniques. To understand how mining systems work, one must also learn how to actualize strategies in R labs, and themes related to automatic calculations, hypothesis etc.

Free Books on AI and ML

To learn machine learning or the use of AI which enables the system to learn from data assimilated without being modified to do so, use the top five free books to help you master ML.
Shai Ben-David and Shai Shalev-Schwartz presentation of Understanding Machine Learning will teach you the basics of ML, its principles, how it uses numerical data to make useful calculations and more. As in the title, it covers all theory regarding algorithms, their standards, neural systems, stochastic plunge slope, developing a hypothesis, ideas, and organised yield learning.
Andrew NG’s Machine Learning Yearning is about getting to be good at AI frameworks building.
Allen B. Downey’s Think Stats will help Python developers understand the subjects and help you make investigative inquiries from data collections.
Other excellent books for beginners to get fluent are Cam Davidson-Pilon’s Probabilistic Programming on Bayesian strategies, derivations and likelihood hypothesis, Trevor Hastie, Jerome Friedman and Robert Tibshirani writings of The Elements of Statistical Learning for learning how to get to unsupervised learning from administered data learning.
There are a vast variety of courses, free materials and visual aids to help with the learning process. The scope for enriching one’s knowledge, especially when required to learn new skills and upgrade one’s knowledge, can never end. Technology is in a state of flux and rapidly changing to embrace newer innovations across more sectors and uses designed to make AI, ML, visualization and deep learning of data and its analytics essential to understand and succeed in business, careers and all fields of applications. It is the will to get there that really matters.

AI Pitfalls: The Reality of Implementing AI

Posted on by Imarticus Learning

Reading Time: 2 minutesThere is no denying the rapid rise of AI. Since 2012, AI has become an almost essential part of every sector of business. 

In medical sectors, AI is making breakthroughs, be it precision surgery, making it safer to go under the knife or in banking, the AI interface has made transactions and customer care-a breeze to walk through, AI is even making its way into the F&B industry with automated smart stoves and microwaves.

Consider most tools you use on a day to day basis have AI, your smartphone now can be unlocked by facial recognition and biometric scanning, both are developments in AI. Your home security system has the same features. You can now enable smart home features using AI products like Alexa from Amazon.

The potential for AI in businesses too is immense, consider that your company can have an automated assistant to perform any task a person would have had to do in the past, this includes making appointments, sending out reminders for important dates and filing.

Artificial Intelligence can also be used for employee recruitment, simply enter specifics into your AI database and let the candidates be chosen for you in minutes. The same can be said of the research, no matter what business you run, you will need research and development models, AI voice search engines like Siri and Alexa in the workplace can streamline research time by providing ready solutions to specific problems.

There is a catch, however, while the potential and benefits of AI are immense, it is important for small businesses to understand that this tech is still in its infancy. Therefore, pitfalls will follow. One of the most common pitfalls for companies looking to integrate or implement AI in their offices is that they are caught up in the hype of the potential of AI rather than what it can currently do for you now.

Another major pitfall to consider when looking at AI for companies is AI management requires an adequate IT team who are knowledgeable in the field, since the field itself is in its infancy, finding adequate management help can be tricky.

One major pitfall to keep in mind is that while AI can reduce costs for operations of a business, it is essential that you don’t depend on AI for all your organization’s solutions, AI has not reached a level of customization where it can solve your company’s unique problems with general solutions. One more major pitfall of using AI in the office is that it can create insecurities amongst the employees, AI still carries an air of mystery which can cause insecurities to the human element in the office, thus creating an unstable working environment.

In conclusion, it is important to remember that AI has immense growth potential and has the ability to streamline your business for the better. It also is still growing including the skill required to manage it. The limitations of AI for your organization specifically and the impact of AI on your employees are all pitfalls you must consider before implementing AI on your company or office specifically.

So before you integrate that AI system to your business, understand what AI can and cannot do for you and your company rather than what it may be able to do in the future due to its potential.
References
https://dzone.com/articles/4-artificial-intelligence-pitfalls
https://www.artificial-intelligence.blog/news/pitfalls-of-artificial-intelligence
https://www.forbes.com/sites/forbeschicagocouncil/2018/06/15/five-ways-ai-can-help-small-businesses/#4d2cead310d7

How AI Can Improve Life Differently?

Posted on by Imarticus Learning

Reading Time: 2 minutesAs more tech companies invest in Artificial Intelligence or AI, the realm of possibilities continues to expand. One of the emerging fields in which AI is on the cusp of making real breakthroughs is with human disabilities. AI is what runs the likes of Siri and Alexa, and this type of development is focused on accessible and inclusive designs.

These tech companies are focused on developing AI in order to make all forms of content and information accessible to everyone. One of the most visible and successful examples of AI for people with disabilities was Stephen Hawking. AI helped him not just continue his research and studies but also helped him impart his vast knowledge while also allowing him movement.

Current Algorithms

Microsoft is one of the largest investors in AI and has spent many hours and dollars on developing new technologies that include people with disabilities. The Microsoft Seeing Ai allows visually impaired people to recognize people, money, text, and more by narrating the world that the user inhabits. Microsoft Hello uses biometric login such as face recognition and fingerprint or iris scans.

This can be particularly useful for people who have physical disabilities or for people who are dyslexic. The FCC has made it mandatory to provide closed captions for speech and sound effects. With machine learning, Google has been able to roll out the same features for YouTube videos. This allows people with hearing impairment to enjoy the full range of the video.

The Future

With about 15% of the world’s population living with some disability and a rapidly aging population in many countries, companies have been invested in finding solutions that help seamlessly. One of the most ambitious ideas is to create robot caregivers. With Honda developing the ASIMO, this future seems more achievable. The idea is to have these caregivers help in order to fit the needs of the individual.

From making coffee to making sure your prescriptions are filled, they will be a valuable asset for many. Another concept that is still in the nascent stage but can prove to be life-changing for those paralyzed is robotic exoskeletons. These can help relieve pressure and even provide movement for the paralyzed region.

What Do We Need?

For AI to truly have an impact on the differently-abled, a lot of forces need to come together with the express idea of including all people. Currently, companies seem to be suffering from a lack of awareness and how their technologies are effectively alienating people. Alexa and Siri are useless for those who have a hearing impairment.

As more companies begin to embrace universal design principles, more people will be able to use these devices. These companies must work with people with disabilities when testing a product. They can do so by working with universities. Lastly, governments need to shake their apathy and start defining the needs of the differently-abled in a social and cultural context.

There is no doubt that Artificial Intelligence can significantly improve the lifestyle of a physically disables person while also providing them with dignity. However, much of the research and development is still at a nascent stage. Working on inclusive design principles will benefit the companies that are on the cusp of AI R&D.

Artificial Intelligence Provides Operational Solutions for the Food Industry!

Posted on by Imarticus Learning

Reading Time: 2 minutesThough Artificial Intelligence (AI) technologies have supported industries in multiple ways, the key is to identify areas specific to each industry where AI solutions are the most relevant. In the case of the food industry, solving operational efficiencies seems to be the area where AI-based solutions can make the maximum impact. And no wonder, with the relatively short timelines that food can be stored before consumption, make this an understandable challenge.

AI or machine learning relies a lot on historic data and uses this information to make predictive solutions or suggestions that can help in foreseeing certain outcomes. The more data at hand, the more closer to accuracy the solution/suggestion is.

Considering this, here are the potential avenues for the use of AI in the food business in ways that could transform conventional modes of operation by increasing efficiencies and production, predicting, assessing, and accurately solving more market demands and more.

Forecasting
Companies have used AI to determine and analyze demand variations, shopping trends during marketing campaigns, and sales drops. Stored data for these variables help machines identify problem areas and solve for them specifically.

It answers questions like – what is the optimal shelf space for this product to ensure increased sales? Which categories perform best for a specific type of promotion? How much should a certain product be stocked during peak/low sales periods? This helps optimize processes and reduce wastes through AI’s intelligent data-back prediction systems.

Boosting Productivity
Cloud computing technology, Big Data analytics, and data-driven machine learning has equipped a lot of industries to streamline their operational efficiencies. In the case of food industries, the manufacturing arm, in particular, AI assists in aiding the production processed by making certain decisions easier through its predictive features. These real-time solutions can potentially save a lot in time and moolah. These cost benefits will in turn be reflected in market satisfaction through pricing.

Automation
Technology’s increased agility in handling fragile produce helps in automating manufacturing tasks in the food industry’s operational chain. This means, tools have become advanced enough to handle delicate food items and process them without damaging them, such as eggs or tomatoes.

Not only this, but automation helps in reducing manual effort in repetitive tasks, therefore, adding time efficiency. This is especially useful for tasks where is lower decision-making potential.

Consumer Preferences
Through AI’s capability to handle large amounts of data with multiple variables and therefore make accurate predictions, consumer preferences can be assessed through their older buying / consuming patterns. Not only does this help in the development of newer products and services but also capitalizes on the key sale-drivers with eagle-eyed consistent focus.

Applications
Some ways to apply AI or machine learning in these industries would be through smartphone apps that fit into the consumer’s lifestyle, such as fitness apps, food suggestion apps based on certain body types, etc. Chatbots for online food partners could be another potential application. Quick food manufacturing machines independent of human assistance is another use case.

Conclusion
What this means for the food industry is that there is a constant need to keep an eye on AI trends and the way it is affecting businesses. Choosing the right AI tool for a certain business is a sure-shot way of intelligently increasing efficiencies and reducing costs. There are many more upcoming AI solutions in the market – keep your eyes on the radar to assess the best solution for your business!