Thursday, January 6, 2022

g-f(2)791 THE BIG PICTURE OF THE DIGITAL AGE (1/6/2022), Nasdaq, The Machine Learning Future Is Now: How AI is Disrupting Entire Industries


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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Artificial Intelligence, How AI is Disrupting Entire Industries (1/6/2022)  g-f(2)426 


OPPORTUNITY, Nasdaq

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The Machine Learning Future Is Now: 

How AI is Disrupting Entire Industries


  • Business FACT: Machine learning and artificial intelligence (AI) are no longer the concepts of science fiction – they’re a $1.41 billion industry that is already making big changes to the way we understand and use immense databases for a wide range of purposes.
  • Disrupting FACT: From supporting cutting-edge cancer research to helping businesses track their inventory, machine learning and AI offer the ability to disrupt and enhance our existing processes in virtually every segment of society.  
  • Opportunity FACT: The global AI space is expected to grow to $20 billion by 2025, according to research performed by Helomics. And it’s not just AI that offers growth opportunities – it’s also the disruption of long-standing industries that machine learning promises. 
  • Market Opportunity FACT: It also carries with it immense market opportunity and the chance to catch the wave of the next big disruption. In fact, 86% of respondents in a 2021 PWC survey said AI technology is now a mainstream part of their company. More than 52% also reported accelerating adoption plans for machine learning and AI technology as a result of the COVID-19 pandemic and its impact on businesses and workplaces worldwide.


Genioux knowledge fact condensed as an image


References


The Machine Learning Future Is Now: How AI is Disrupting Entire Industries, Predictive Oncology, January 6, 2022, Nasdaq.


ABOUT THE AUTHORS


Predictive Oncology


Predictive Oncology Inc. (POAI) is a knowledge-driven company focused on applying artificial intelligence (AI) to develop personalized cancer therapies, which can lead to more effective treatments and improved patient outcomes.


Extra-condensed knowledge



Lessons learned, Predictive Oncology


Some Golden Knowledge (GK) Juice on supporting cutting-edge cancer research 


  • How to Develop More Effective Cancer Treatments
    • It’s never been a chip shot developing cancer treatments. In fact, only 7.78% of all cancer drugs that enter phase 1 clinical trials are ultimately approved by the U.S. Food and Drug Administration (FDA), according to the Pharmaceutical Manufacturers Association. Of the 1,481 cancer drugs selected for clinical trials between 2000 and 2020, only 115 were approved. In a world where that process costs nearly $650 million on average for a single cancer drug, a 92.22% rate of failure means billions of dollars and thousands of hours wasted. 
    • Why is it that so many cancer drugs fail in clinical trials? And how can modern technology like artificial intelligence, 3D cell cultures, and novel approaches to tumor research improve the odds? After more than a century of research, we’re finally on the cusp of improving our ability to effectively discover and develop safe, effective cancer drugs not only for specific types of cancer, but also specific types of patients.
  • Using artificial intelligence and machine learning to improve cancer treatments
    • The issue with cancer research historically is that it simply isn’t feasible, even for teams of experts, to possibly account for the many factors that influence how a tumor develops and grows in a particular patient and then account for which drug formulations will best treat it. That’s because the human mind can only handle so much stimuli.
    • Machine learning and artificial intelligence can process information so many orders of magnitude faster than human beings that it can complete countless scenarios, predicting cancer treatment outcomes with various different drug formulations, before a team of human experts has played out even a single scenario. And it can do that around the clock, 24/7/365, getting sharper and smarter as it goes. AI can improve cancer research by rapidly considering more factors about the cancer, the patient, and the treatment than humanly possible.
    • That’s precisely what Predictive Oncology is doing with its team at Helomics, applying three proprietary machine learning algorithms to a massive database of more than 150,000 de-identified patients, 131 types of tumors, and 30 different types of cancers. These algorithms — known as CoRETM, PeDALTM, and TruTumor™ — are able to analyze all this data to determine: 
      1. the top optimal drug formulations
      2. for the individual patient based on their genetic background and lifestyle
      3. based on the type of cancer and heterogeneous make-up of the tumor in its current stage. 



    Condensed knowledge




    Lessons learned, PWC survey 

    Some Golden Knowledge (GK) Juice on AI Predictions 2021


    • How to navigate the top five AI trends facing your business 
      • Despite a tough year for many, US companies are accelerating plans to implement artificial intelligence (AI). A quarter of the companies participating in our latest AI survey report widespread adoption of AI, up from 18% last year. Another 54% are heading there fast. And they’ve moved way beyond just laying the foundation. Many are reaping rewards from AI right now, in part because it proved to be a highly effective response to the challenges brought about by the COVID-19 crisis. In fact, most of the companies that have fully embraced AI already report seeing major benefits.
    • Yet a painful fact persists. AI is hard.
      • Too many AI investments end up as “pretty shiny objects” that don’t pay off. Most companies have yet to adapt talent strategies, organizational structures, business strategies, development methodologies and risk mitigation for a world that moves at AI speed.
      • So there’s work to be done, but the reward can be concrete benefits today and the foundation for success tomorrow. As we’ve done for the last four years, we’ve made key predictions informed by our survey of more than 1,000 executives (including over 200 CEOs) at US companies that are using or considering AI. Together, these insights should help your company navigate the top AI trends it will face in 2021 and beyond.
    • Learn about the five predictions and what your company can do to make the most of AI
      1. No uncertainty here
        • This trend is crystal clear. US companies are ramping up their AI investments. Fifty-two percent of our survey respondents have accelerated their AI adoption plans in the wake of the COVID-19 crisis. The results will be felt for years to come. These “accelerating” companies cite their top changes as new use cases for AI (40%) and increased AI investments (also 40%). Of all the participants in our survey, 86% say that AI will be a “mainstream technology” at their company in 2021.
      2. Your strategic ally
        • The fastest way to get return on investment (ROI) is to use AI’s advanced automation capabilities to improve efficiency and productivity. Understandably enough — who doesn’t like fast ROI? — that’s the top goal for AI strategies.
        • But increased innovation and revenue growth are also rising in importance, and that requires making AI an ally in strategic decisions. Fifty-eight percent of our survey respondents have increased investments in AI for workforce planning, 48% are ramping up investments for simulation modeling and supply chain resilience, 43% are upping investments in AI for scenario planning and 42% for demand projection.
        • Together, these investments can make AI a strategic ally, closing the gap between idea and execution to drive faster and better decisions.
      3. From risk awareness to risk action
        • The good news about AI’s risks? Companies are aware of them. The bad news? Most are not actually mitigating them. When we asked our survey respondents for their top-three priorities for AI applications in 2021, the top choice (picked by 50%) was responsible AI tools to improve privacy, explainability, bias detection and governance. But when it comes to action, only about a third reported plans to make AI more explainable, improve its governance, reduce its bias, monitor its model performance, ensure its compliance with privacy regulations, develop and report on AI controls, and improve its defenses against cyber threats. In the case of explainability, companies have even taken a step back compared to our 2020 survey.
      4. Beyond upskilling
        • Upskilling is necessary, but it’s not nearly enough to match the demands of an AI-centered workplace. Net job growth is predicted to be a long-term impact of AI, but these jobs will be different from the ones that have existed in the past. Business leaders need to reevaluate exactly what they’ll need from this future workforce. 
        • Ever-changing, continuously learning AI means that agile software development, with its linear, iterative approach and rigid handoffs, won’t work. Instead, AI teams have to be constantly testing, experimenting and learning — like scientists. With time, this approach will have to guide not just your AI and technology teams, but also your entire workforce. Your company can get there, but it has to act now.
      5. The model is never done
        • The top choices for 2021 AI and analytics priorities all — inevitably — have one thing in common: They cross the entire organization. That’s because AI does too. Unless your company is already effectively sharing data, subject matter expertise, governance and AI models across teams and functions, you’re going to have to reorganize so that you can collaborate as needed.
        • AI reorganization goes beyond breaking down silos. It also requires a cultural shift so that everyone’s decisions become more based on data — and the simulations and forecasts that AI produces from that data. It also requires integrating machines that think and learn — and teach themselves to learn even better — into your organization. When AI models are constantly improving themselves, your decisions can constantly improve as well. Your company will need to be ready to pivot quickly, not on a yearly planning cycle, but few organizational flow charts are currently set up for that kind of speed.



    Some relevant characteristics of this "genioux fact"

    • Category 2: The Big Picture of the Digital Age
    • [genioux fact deduced or extracted from Nasdaq + Predictive Oncology + PWC]
    • This is a “genioux fact fast solution.”
    • Tag Opportunities those travelling at high speed on GKPath
    • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
    • Type of validity of the "genioux fact". 

      • Inherited from sources + Supported by the knowledge of one or more experts.


    References


    “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)791, Fernando Machuca, January 6, 2022, blog.geniouxfacts.com, geniouxfacts.com, Genioux.com Corporation.


    ABOUT THE AUTHORS


    PhD with awarded honors in computer science in France

    Fernando is the director of "genioux facts". He is the entrepreneur, researcher and professor who has a disruptive proposal in The Digital Age to improve the world and reduce poverty + ignorance + violence. A critical piece of the solution puzzle is "genioux facts". The Innovation Value of "genioux facts" is exceptional for individuals, companies and any kind of organization.



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