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Can the forecasted weather be used to predict the winning race time?

My hypothesis is that the weather plays a major impact on the Kentucky Derbys winning race time. In this analysis I will use the Kentucky Derbys forecasted weather to predict the winning race time using Machine Learning (ML). In previous articles I discussed the importance of using explainable ML in a business setting to provide business insights and help with buy-in and change management. In this analysis, because Im striving purely for accuracy, I will disregard this advice and go directly to the more complex, but accurate, black box Gradient Boosted Machine (GBM), because we want to win some money!

The data I will use comes from the National Weather Service:

# Read in Data #data <- read.csv("...KD Data.csv")

# Declare Year Variables #year <- data[,1]

# Declare numeric x variables #numeric <- data[,c(2,3,4)]

# Scale numeric x variablesscaled_x <- scale(numeric)# check that we get mean of 0 and sd of 1colMeans(scaled_x)apply(scaled_x, 2, sd)

# One-Hot Encoding #data$Weather <- as.factor(data$Weather)xfactors <- model.matrix(data$Year ~ data$Weather)[, -1]

# Bring prepped data all back together #scaled_df <- as.data.frame(cbind(year,y,scaled_x,xfactors))

# Isolate pre-2023 data #old_data <- scaled_df[-1,]new_data <- scaled_df[1,]

# Gradient Boosted Machine ## Find Max Interaction Depth #floor(sqrt(NCOL(old_data)))

# find index for n trees with minimum CV errorbest.iter <- gbm.perf(tree_mod, method="OOB", plot.it=TRUE, oobag.curve=TRUE, overlay=TRUE)print(best.iter)

In this article, I chose a more accurate, but complex, black box model to predict the Kentucky Derbys winning race time. This is because I dont care about generating insights or winning buy-in or change management, rather I want to use the model that is the most accurate so I can make a data driven gamble. In most business cases you will give up accuracy for explainability, however there are some instances (like this one) in which accuracy is the primary requirement of a model.

This prediction is based off forecasted weather for Saturday May 6th, taken on Thursday May 4th, so obviously it should be taken with a grain of salt. As everyone knows, even with huge amounts of technology, predicting weather is very difficult. Using forecasted weather to predict the winning race time adds even more uncertainity. That being said, I will take either the over or the under that matches my predicted winning time of 122.12 seconds.

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Using Machine Learning to Predict the 2023 Kentucky Derby ... - DataDrivenInvestor

Its happening already. Following Dropboxs move to lay off 500 employees as it shifts its focus to AI, IBM now plans to replace 7,800 jobs with AI technology and pause hiring for roles that could be automated. Company CEO Arvind Krishna stated that most back-office positions, such as HR and accounting, will be replaced.

Layoffs due to AI were inevitable, but amid lingering job losses, new jobs are also being created. A report by the World Economic Forum states that demand for AI and machine learning specialists will grow at the fastest rate in the next five years. The organisation has also listed a number of AI jobs that are expected to see massive growth in the coming years. Lets take a look at them.

AI and machine learning specialists: These are professionals who design, develop, and implement AI and ML systems and applications. They use various tools and techniques to analyse data, build models, and optimise algorithms. The demand for AI and machine learning specialists will grow at the fastest rate in the next five years, the WEF report says.

Big data specialists: They specialise in managing, analysing and interpreting large and complex data sets. They use cutting-edge technologies to organise, store, and retrieve vast amounts of information, turning it into valuable insights that can drive business decisions. They work with a variety of industries such as healthcare, finance, and technology, to help them understand and leverage the power of data.

Data engineers: They are responsible for the design, construction and maintenance of the data infrastructure that supports an organisations data management and analytics needs. They develop and manage data pipelines, work with large datasets, and ensure that data is available and accessible to those who need it. They also work with other data professionals to design and implement data architectures that meet the needs of the organisation.

Data analysts and scientists: These are experts who collect, process, and interpret large and complex datasets to generate insights and solutions for various problems and domains. They use statistical methods, programming languages, and visualisation tools to manipulate and communicate data. Data analysts and scientists are expected to see a 32% growth in demand by 2023.

Apart from the aforementioned jobs listed by the World Economic Forum, heres a list of other jobs AI is expected to create in the near future.

AI trainers: They are responsible for teaching machines to learn from data effectively. They also help to ensure that the AI models accurately interpret the data, providing businesses with valuable insights that can drive informed decisions.

AI ethicists: They use their expertise to ensure that AI systems are developed and deployed responsibly. They also identify potential ethical concerns related to privacy, fairness, and transparency, and work to address them through policy and guidelines.

AI user experience designers: They create interfaces and experiences that are intuitive and user-friendly for AI-driven products and services. They also work to ensure that users can easily interact with AI systems, making their experiences more enjoyable and productive.

AI security analysts: They focus on ensuring the safety and integrity of AI-driven solutions. They also identify potential threats, vulnerabilities, and attacks that could compromise AI systems and develop strategies to mitigate them.

Robotics engineers: They design, build, and program autonomous machines that can perform a wide range of tasks, from assembly line work to surgical procedures. By incorporating AI capabilities such as computer vision and natural language processing, they create intelligent machines that can work alongside humans in new and exciting ways.

Of course, these are just a few examples of the new jobs that AI is expected to create. As AI continues to evolve and become more integrated into various industries, its likely that even more new job opportunities will emerge.

IE Online Media Services Pvt Ltd

First published on: 03-05-2023 at 19:39 IST

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From machine learning to robotics: WEF report predicts the most lucrative AI jobs - The Indian Express

Mosaic is thrilled to be recognized as a leader in the Artificial Intelligence & Machine Learning space.

LEESBURG, VA / ACCESSWIRE / May 1, 2023 / Mosaic Data Science is pleased to have been featured in the latest issue of CIO Review as the cover story of their AI and Machine Learning edition. With this feature, the publication has named Mosaic as the Top AI & Machine Learning Company of 2023 award. The article notes that recent advancements in artificial intelligence have placed organizations on the brink of a new era of automation, but its full potential is yet to be realized. With the global AI software market expected to grow rapidly in the coming years, reaching around $126 billion dollars by 2025, Mosaic is well-positioned to offer customers a practical approach to data - beyond the hype.

For years, the hype surrounding AI has far outweighed its transformative impact, with vendors often overpromising and under-delivering on what AI could accomplish. As a boutique consultancy and services firm, Mosaic helps organizations take a more nuanced approach to AI adoption. CIO Review highlights Mosaic's ability to combine AI and ML with customized scoping and targeted problem-solving to help organizations unify their efforts and achieve their digital transformational goals.

"The wealth of experience, knowledge, and problem-solving capabilities of Mosaic's team are the bedrock of its sustained success and remain the driving force behind the company's ongoing growth," the article reads.

According to a study by Oberlo, AI use is responsible for optimizing high-level business productivity by up to 54%. Mosaic's services are designed to provide businesses with a comprehensive approach incorporating analytics depth, domain expertise, and flexible engagement models to help clients gain a competitive edge in their respective industries, such as professional and financial services, retail, manufacturing, CPG, and oil and gas.

"Our portfolio of solutions come from repeat patterns in successful project execution," said Mike Shumpert, Managing Director at Mosaic. "We don't start by gathering ingredients and brainstorming what we can make with them; we write a cookbook after we've cooked many great cakes. We do this by using insights from past projects, your current environment, and use case requirements to facilitate ease of use and increase time to insight."

As technology continues to transform businesses, the time is now to prioritize an AI-led strategy that enables innovation, human creativity, and business growth. CIO Review notes that by partnering with Mosaic, business leaders can leverage the power of purpose-built AI and ML solutions to transform their organizations, enable better decision-making, and help find answers to questions they didn't even know they should be asking.

You can read the full article here.

About Mosaic Data Science

Mosaic Data Science is a leading AI/ML services company focused on helping organizations build and deploy custom solutions. The company makes complex artificial intelligence and machine learning solutions actionable, explainable, and usable to any organization.

Contact Information

Drew Clancy VP of Marketing and Sales [emailprotected] (410) 458-7674

SOURCE: Mosaic Data Science

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Mosaic Data Science Has Been Named the Top AI & Machine ... - AccessWire

In episode 105 of the AI/Hyperautomation Minute, Toni Witt provides clarity behind generative AI, its underlying technology the GPT (generative pre-trained transformer) machine learning model and how its evolving.

This episode is sponsored by Acceleration Economys Generative AI Digital Summit, taking place on May 25. Registration for the event, which features practitioner and platform insights on how solutions such as ChatGPT will impact the future of work, customer experience, data strategy, cybersecurity, and more, is free. To reserve your spot, sign up today.

00:26 While there are many conversations about generative AI, those outside of the tech field may still have a misunderstanding of the underlying technology and how its evolving.

01:03 Toni clarifies that ChatGPT is an web-based tool that gives access to GPT-3, which is the underlying machine learning model. GPT-3 is a word predictor. Its a form of deep learning with capabilities that are essentially a subset of what machine learning and AI can do.

01:37 Machine learning started with prediction and classification. Most AI applications that give returns to companies are these classification or predictor models, Toni explains. The Netflix recommender algorithm is an example of this, as it uses data from previous movies and shows that youve liked in the past to recommend what to watch next.

02:12 GPT-3 is a transformer model. Theres a pretty big debate going on whether these transformer models are going to be the ones that reach what you might call AGI, or artificial general intelligence, that basically matches the intelligence level of a human, Toni says.

02:57 Sam Altman, CEO of OpenAI, pointed out a trend that there will be base-level models. The GPT series is already an indication that models will help train other models. Think of it like a tech stack, says Toni.

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How the GPT Machine Learning Model Advances Generative AI - Acceleration Economy

Jenny Arden, Chief Design Officer, Zillow

Generative AI is the topic du jour and for good reason. The recent explosion of new generative tools that are fun and powerful is bringing the AI conversation to the forefront. But generative AI is justoneapplication of this tech. In reality, AI has been around for decades, transforming industries and improving customer experiences in many impactful, though less obvious, ways. And the biggest strides are yet to come.

Real estate, for example, does not yet utilize the full potential of technology. Its a complicated and antiquated industry with layers of outdated rules and practices, making it challenging to combine all the pieces. But constant artificial intelligence and machine learning advancements will transform real estate technology. In 2006, when Zillow launched the Zestimate home valuation model to make real estate more transparent, it was revolutionary. And itcontinues to evolve. But what people need and what Zillow is creating are ways to more quickly and easily find a dream home they can afford.

For the first time in real estate, AI is also powering smarter, easier ways to search for homes. Zillow recently launcheda natural language search, which can interpret colloquial lingo. Now, instead of checking boxes and selecting filters, home shoppers on Zillow can search as if they are talking with a friend or agent. While natural language search isnt the pinnacle of what were trying to achieve through AI or novel for the tech industry as a whole, it is a real estate industry first. And by allowing people to search for a home using words they would use with their agent or a friend, we are taking an important step to make real estate more accessible and allow all communities the ability to engage with technology.

Going beyond searching, Zillow has an opportunity to help people find great homes they either didnt know how to find on their own or would have taken a lot of hunting around to discover. AI-driven personalization can put a shoppers dream home right in front of them. Powered by machine learning models, features like saved and hide homes" learn about buyers' preferences as they consider each listing to create a more personalized experience, then highlight homes they havent seen yet that theyll likely love.

And lastly, AI is also changing the way shoppers tour homes.AI-powered interactive floor plans, powered by 360-degree photos captured by Zillow and then stitched together to build an immersive touring experience, are a remarkable leap forward for buyers trying to understand what a home feels like before they tour in person. Whats more, the technology automatically creates an accurate floor plan, allowing a shopper to get a real sense of the layout, size, and scale of a home from their phone or computer. This has the power to save many hours of agents' and home shoppers time to confidently narrow down and tour only the best options instead of driving to and touring homes they could have ruled out from their couches. The goal, of course, is for this to become an industry standard, but it wont stop there. Imagine a world where an online listing could show you lighting inside a home throughout the day or replicate the sounds you might hear. What if AI could show you your own furniture in the house or dress up rooms in your preferred interior style? The possibilities are endless and immensely exciting for shoppers embarking on what is likely the most important financial decision in their lives. Anything we can do to help shoppers visualize their future life in space will bring them one step closer to being confident theyve found the right home for them.

While generative AI technology is in its infancy, AI as a whole has the power to unlock the future of real estate, with Zillow at the forefront of the transformation. AI will play a key part in delivering practical value to our customers by knowing what home shoppers want and responding with a solution that feels closer to how people think and talk about their next home. And with asuper housing appto make the process easier, Zillow will streamline the transaction and get more people into a home they love.

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Artificial Intelligence And Machine Learning Will Power The Digital ... - CIOReview