Contact Us
×

Tag: Artificial Intelligence courses

Can You Integrate AIML with Android App?

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Artificial Intelligence has quickly become one of the most important fields to humanity today. The subject of an increased amount of research, AI is currently one of the few fields which are soaring with no end in sight today. It can be said that the very future of humankind now depends upon AI, and how it develops in the future – such is the reach of Artificial Intelligence in the modern world.

With such a rapid rise in the field of AI, there is no doubt that the demand for talented people in the field is higher than ever. If you want a career which is challenging yet satisfying, Artificial Intelligence is definitely one of the best options. However, you should start learning more about AI quickly, and what better way to put your skills into test than building a chatbot?

Chatbots are one of the latest sensations sweeping over AI practitioners. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other. Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence. A conversational assistant is a challenge to create because it has to give a new answer to the same questions and learn from the answers of the user, too. You can build simple chatbots with ease, and port it into android apps too, in many ways.

AIML was one such language which was used in the development of early chatbots.

What is AIML?

Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch. AIML is an extremely simple XML, just like HyperText Markup Language or HTML.

It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

Steps to Integrate Chatbots into Android Apps

The steps covered here are not comprehensive in any way, but only an outline which you can follow in order to make what you want. These do not contain any codes, because that would defeat the purpose of creating an android app chatbot from scratch.

However, you can always skip the parts you are uninterested in, like the design aspects of the app and the likes.

The first step is to create a chat UI and interface using Android Studio. Using XML, you can do this with only a basic understanding of the language. It should have an adapter too, for the different view types.

The, import the AIML files that you have written beforehand to your app. Then, the task you have is to modify the MainActivity.java in such a way so as to include the class Bot in it.

Obviously, there is a lot of coding involved if you want to build the bot from scratch. However, integration is definitely possible, too. If you find yourself interested in learning more, you should check out the artificial intelligence courses in India on offer at Imarticus Learning.

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.

Getting on the Right Artificial Intelligence Path

Posted on by Imarticus Learning
Reading Time: 2 minutes

 

Are you looking to expand your current skill-sets? Does Artificial Intelligence pique your interest? Artificial Intelligence uses software or machines to use intelligence similar to that of humans. Even the humble calculator is an example of artificial intelligence. The field of AI is currently focusing on creating systems that can reason, learn, present knowledge, plan, and understand natural language amongst many others.

If you want to jump into this new and exciting field of innovation, you might want to make sure that you have your basics covered. There are several artificial intelligence courses in India that you can enrol in. However, if you are looking to explore on your own, you can follow the path given below to give you an understanding of how AI functions.

Brush Up On Your Math
A strong understanding of mathematics is key to your ability to move forward in the field of AI. Knowing as much math as you can will definitely help you later, but at the start, you can focus on statistics, calculus, and optimization. There are several resources available online for these topics, and you can also brush the dust off your old math textbooks.

Learn A Language
No, we don’t mean French. You need to learn the right programming languages in order to be able to delve into Artificial Intelligence. Focus your time on learning Python, C, and C++. These languages come with well-stocked toolkits and libraries which will help you navigate your future projects. Each of these languages has their own benefits and limitations, but starting with Python is a good bet. Look up artificial intelligence online courses offered by Imarticus Learning.

Solve a Problem you Know
One of the best ways to get started on AI is to practice with a problem you know and are interested in. It will keep you motivated as you continue to delve deeper into the intricacies of AI. The problem should interest you and must come with ready to access data that can be worked with a single machine. You could also start with the Titanic Competition that is tailormade for beginners like you.

Make Your Own Bot
A BOT is a type of weaker AI that can complete automated tasks. Try your hand at building your very own Chatbot. An example of an advanced chatbot is the Google Search Engine. It has three basic components – input text, send button, and output text. You can explore open source platforms like XPath and Regex in order to build your very own chatbot. This chatbot can be complex or funny and helpful. You can choose what your bot does for you.

Participate in an Actual Kaggle Competition
Kaggle has many real-time competitions that see hundreds of enthusiasts try to solve a problem. You can test out your knowledge and also learn where you need to explore more. This opportunity also allows you to connect to other AI enthusiasts. The forums are a rich resource on problem-solving and debugging.

Free Resources
There are many places on the internet and artificial intelligence courses which will help you expand your knowledge of AI one skill at a time. A great free resource is the Intel AI Academy which provides much-needed support, tech, and other tools for beginners like yourself.

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!

Can Artificial Intelligence be self-aware?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Artificial Intelligence has gradually been spreading its wings to more and more sectors wherein only humans could work until now. A prime example of this is the introduction of an artificial intelligence process to vehicles, making self-driving cars a reality.

While this is a significant development, it pales in comparison to the possibilities that could be unlocked if we have a computer that is completely aware of itself and its surroundings. These machines could be sent to do a variety of jobs that humans find difficult.

In fact, it could reach such a point wherein robots replace humans at every job, leaving humans to live their lives in lazy bliss. This could completely shake up society as we know it and leave humans without a sense of purpose. This also raises the question of legality when it comes to robots. Would be held under the same accountability as humans, or scarily still, would they band together and eliminate humans altogether?

There are varying views among researchers about what consciousness is and whether machines could someday achieve it. Some researchers say that consciousness develops with constantly accepting new information, retrieving the old and processing all of it into thoughts and, subsequently actions. If this assumption is true, then any consciousness developed by computers will be the most advanced one, even more so than human consciousness.

They will be able to access millennia worth of information within a fraction of a second and be able to make decisions which are both more complex and more logical than what any person could accomplish. However, some researchers disagree with this opinion,saying that some factors that contribute to consciousness, such as creativity and compassion, are not the result of calculations and will always be exclusive to humans.

Another view of this topic is the quantum view, which takes into account the quantum theory of physics. According to this view, all the physical aspects of this world and consciousness complement each other all the time. It goes on to say that whenever a person observes or manipulates any physical object, noticeable change could be observed due to that person’s conscious interaction with the object.

This can be explained by considering consciousness as something that exists by itself and isn’t derived through physics, merely needing a medium such as a brain to manifest itself. If this is true, it seems highly unlikely that machines would be able to tap into this consciousness.

Another factor to talk about here is computational programming, which is the basis on which every computer on the planet runs. For a computer to be self-aware, it should have the capability to come up with its own language as a means of processing thought through artificial intelligence training course. However, while this has been done by artificial intelligence processes in recent years, these languages still depend on and are based on the instructions given to these machines by humans.

As such, these machines will never be able to reach the same level of thought complexity that would be required to say, write a poem, or even understand empathy and a sense of belonging to the world. So in short, the answer to the question of whether artificial intelligence could become self-aware is, yes but only to an extent.

The likelihood of these machines reaching the level of thought complexity that humans enjoy is extremely less unless there is a breakthrough in our knowledge of how our minds work and we are able to translate that into the code for these computers.

Why Knowing Python Is Essential For AI And Machine Learning?

Posted on by Imarticus Learning
Reading Time: < 1 minute

Getting started in a field like machine learning or artificial intelligence can be a challenge. Due to the numerous coding mechanism sand tools available to help you program your potential AI software, using an open-source tool like python is considered an essential skill. Python is one of the easiest coding languages around today and is also one of the most versatile and wide-spreading tools available.

To learn Machine Learning and AI you will need a specific language. There are several languages that one can learn including C++, Java, and R. However, most industry experts agree that Python is one of the best places to start. It has a well-stocked library and comes with an extensive and diverse toolkit.

Here is a closer look at how you can chart your learning of Python for Machine Learning and AI.

  1. Learn the Syntax

Python is all about the various syntax. The good news is that you do not have to learn all of it. However, there is no getting around learning the basic syntax of Python. With this step, it is recommended that you do not spend too much time on it. A few days, up to a week, is enough to learn the basic syntax of Python as you can always refer to it later.

There are many places on the web where you can familiarize yourself with artificial intelligence courses including on the main Python website.Other web pages include Imarticus Learning who teach Python with learning data science as the end game.

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.

7 Answers to the Most Frequently Asked Questions About Artificial Intelligence

Posted on by Imarticus Learning

Reading Time: 3 minutesOne of the most trending topics in the today’s world is Artificial Intelligence. From self-driving cars to Siri, the world is now overwhelmed with automation. Technically, Artificial Intelligence is seen as a robot possessing characteristics similar to humans. However, AI has a lot more to offer. IBM Watson or automated weapons, facial recognition or search algorithms, AI explores every aspect of human life.
Hearing so much of this much-hyped technology, it is obvious that a pool of doubts strikes your mind. From what to who and how you might be too curious to know what Al does and has to offer. Here, we outline 7 most of the frequently asked questions about Artificial Intelligence.
Questions:

  1. Why is the need to employ artificial intelligence?

 Ans: Artificial Intelligence is an abode of automation. Till date, programmers required to hardcode the instructions in files to have the robots working. But, with the advent of machine learning & Artificial Intelligence courses, IT Industry has taken a steep turn. Now, we expect the programs to learn rather than simply follow what it has been coded with. Myriads of tasks that seek human intelligence are effectively automated with Artificial Intelligence. It is simply a pool a data. The more, the merrier. Health care, education, agriculture, and business are few industries that have readily adopted this new technology and driving benefits too. Cutting unnecessary human labor by replicating them with AI made machines. Also, jobs that pose threat to human life can be efficiently done with the help of AI.

  1. Why do you need to study Artificial Intelligence?

 Ans: Before we head towards the prime need to study AI, let’s see it’s application. Siri or Google assistant are not just fun but cuts downtime of typing while surfing something. Playing chess with a virtual opponent seems amazing. Agree? Do you have an idea that Google translate employs AI or the spam blockers incorporating AI?
We all use it but are not aware of it. Studying AI would not just open up a pool of job opportunities for you, but on s lung run turn you more subjective towards things you notice or witness. You would be able to link technologies better. AI is such a subject that is ever evolving and hitting a job in such a domain would change your life.

  1. How can we apply AI?

 Ans: In order to use AI, you don’t need to be a programmer or work in any IT industry. We are presently using products of AI and they have genuinely made our life simpler and easier.

  • Siri: The smart assistants are used almost every day. Though they are a little slow, yet it is expected by 2025, they will gradually occupy the business world.
  • Chatbots: The most effective application of Artificial Intelligence is learning ways to build chatbots. Currently, chatbots are based on rules and do not employ AI. So, the near future would definitely look forward to the implementation of AI in the creation of Chatbots.
  1. What kinds of jobs are related to AI?

 Ans: There induction of AI has opened doors for Myriads of job opportunities. What we lack is the resources to fulfill the need. Few jobs directly related to the field of AI include:

  • Data Scientist
  • Research Scientist
  • Machine Learning Experts.
  • Deep Learning Experts
  • Software Engineers.

In the future, there would be a need for chat bots designer, business strategy consultant and many more opportunities for a job.

  1. Name some powerful agencies inducing Artificial Intelligence?

 Ans: Seeing such a massive growth in the field of AI, almost all companies are now thriving to rank one in the underlying field. However, the major ones include:

  • Google
  • Amazon
  • Microsoft
  • Facebook
  • IBM
  • Apple

Google leads all of the above mentioned and apple being the weakest. Amazon Alexa software makes it rank in after Google.

  1. What are the benefits of AI technology?

 Ans: AI is expected to change the life of all across the globe dramatically. It could be direct or indirect but the benefits are huge. Some of the key benefits are listed below.

  • AI is expected to facilitate better life reducing poverty.
  • Dangerous tasks can be efficiently performed by AI.
  • AI would automate vehicles, thereby easing travel.
  • AI would create a pool of opportunities for business professional.
  1. Would AI be a threat?

 Ans: This has a twofold answer. Where few consider it as a threat to eliminating the need for human sole believe it would create job opportunities. Some consider to would ease life, few think it would create endless issues. Now, what you believe depends on you. However, everything is a threat is over-utilized. Agree?

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