Machine learning and artificial intelligence is transforming the healthcare industry as we know it. Especially in the last year and half, being stuck inside one’s house has led to the manifold increase in the number of people seeking out for virtual wellness services. As a result, the wellness industry is said to increase further over the next four years. A growth of $1299.84 billion is expected between last year and 2024, at a CAGR of 6.37 percent during the forecast period.
Read more: https://analyticsindiamag.com/ai-for-meditation-how-headspace-leverages-ai-and-ml-technologies/
Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.
Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.
While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.
On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.
However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.
#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap
If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.
AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.
#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution
AI technologies are becoming standard across industries. Organizations are exploring select opportunities and implementing a few use cases while becoming an AI-fueled organization. Though this is a welcoming initiative for AI implementation, an organization must rethink the way they are implementing technology in their business so that they can be called the market makers.
It is critical to consider AI tools, machine learning techniques, and other cognitive tools to deploy systematically across every core process. Implementing AI will become a strategy and fundamental expectation in due course. At this juncture, the organizations must have a strong AI team that can drive new offerings and business models.
Let us understand the AI team in detail here for the survival of the business.
BUILDING AN AI TEAM FOR YOUR ORGANIZATION
The AI-fueled journey presents a straightforward proposition of keeping AI, ML, and other cognitive technologies at the center of business. AI helps to improve existing products, optimize internal and external operations, make better decisions, and give space for employees to become more creative.
To achieve this quantum leap, it is necessary to possess a strong AI team. It is critical to approach AI with a process-level view so that you can deploy them in a way that it significantly brings successful changes and improve customers’ experiences. Though you may start small, your team must have these AI professionals to be successful.
Here isa list of must-have professionals in your AI team.
The organizations that have successfully implemented AI have strong leadership support. A C-suite professional who can take an active role in aligning and ensuring AI projects with the company’s business strategy is of utmost importance. The C-suite professional is responsible for partnering with solutions providers, obtain funding, and taking the right initiative in the AI journey. This person acts as the real bridge between technology and business by driving AI pilot projects that deliver a measurable return on investment (ROI).
Artificial Intelligence Researcher (AI Researcher)
30 percent of IT executives find AI researchers as their top priorities than any other role, Deloitte’s survey reports. The research and development (R&D) department in an enterprise is not a luxury, but a necessity.
For instance, AppTek had to invest in research to transform and keep up their specialization in human language technology.
#ai technologies #artificial intelligence #ai researchers #ai #ml
In this post let us talk about the types of agents and challenges of data set for the agents.
All agents have the same skeletal structure. They get percepts as inputs from the sensors and the actions are performed through the actuators. Now the agent can either just act on a percept as a reflex for example if you throw a ball at me and I try to catch it (or duck from it given that I am bad at baseball) than that is a quick reaction to the percept. On the other hand if you throw a ball at me and tell me to arrange it by color or count the number different colors that you are throwing at me then I would have to maintain a state to do the counts correctly. So this would involve some state but is still ok. Now, if you want to trouble me further and you tell me to jump twice if you throw red ball at me and do a burpee if you throw a green ball at me apart from catching and counting then you got me for sure
This would involve a complex logic of me maintaining a mind table of what needs to be done on what percept and this is called** condition-action rule**. Now if all these percepts were to be indexed then this would become a significant data set.
Consider the automated taxi: the visual input from a single camera (eight cameras is typical) comes in at the rate of roughly 70 megabytes per second (30 frames per second, 1080 × 720 pixels with 24 bits of color information). This gives a lookup table with over 10 600,000,000,000 entries for an hour’s driving. Even the lookup table for chess—a tiny, well-behaved fragment of the real world—has (it turns out) at least 10 150 entries.
This can become a lot of information.
The key challenge for AI is to find out how to write programs that, to the extent possible, produce rational behavior from a smallish program rather than from a vast table.
So this brings us to 4 documented types of agent programs
#ai #ml # ai and data engineering #scala #ai and ml
Corona Virus Pandemic has brought the world to a standstill.
Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.
Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!
And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!
If you are still not aware of it, don’t worry as Georgia Byng has well said,
“No time is better than the present”
– Georgia Byng, a British children’s writer, illustrator, actress and film producer.
No matter if you are a developer (be it front-end or back-end) or a data scientist, tester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.
From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.
Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.
These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.
Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).
xlr8rs make your development real fast by just adding your core business logic to the template.
If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂
To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.
Since we believe:
“There’s always a scope of improvement“
If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.
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