Analyzing Medical Images: Detecting Pneumonia with Custom Vision AI
Recently we had the 17th edition of our Data & AI Meetup. This meetup focused on Data & AI in Healthcare. Let’s have a quick recap!
Agenda:
16:00 – Willkommen & Intro
16:05 – BI as a Service für eine bessere Healthcare Supply Chain
Christopher Glogger, Sana Einkauf & Logistik
16:35 – AI Trends und Use cases
Andreas Kopp, Microsoft
17:20 – Visuelle Datenanalyse rund um CoViD-19
Markus Raatz, Ceteris AG
17:55 – Wrap up
Explore the intricate relationship between AGI and GPT models like OpenAI’s GPT-3, as revealed in the much-awaited book „Decisively Digital.“
Artificial Intelligence (AI) has been making significant strides in recent years, particularly in the realm of generative AI. Among these advancements, OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) has emerged as a groundbreaker. While its language generation capabilities are astonishing, the question remains: Are we any closer to achieving Artificial General Intelligence (AGI)? In this article, we’ll explore the complex world of GPT-3, its potential, and its limitations, as discussed in my forthcoming book Decisively Digital.
The Evolution of Generative AI and GPT-3’s Arrival
Generative AI has seen considerable growth in recent years. OpenAI first introduced GPT-3 in a research paper published in May and subsequently initiated a private beta phase. Selected developers have been granted access to further explore GPT-3’s capabilities. OpenAI has plans to turn this tool into a commercial product later this year, offering businesses a paid subscription to the AI via the cloud.
The Capabilities of GPT-3
The evolution of large language models like GPT-3 is worth examining in the context of Natural Language Processing (NLP) applications. From answering questions to generating Python code, GPT-3’s use cases are expanding by the day. Generative AI has been escalating at an unprecedented rate. OpenAI’s recent launch of GPT-3 has created a buzz in both the tech community and beyond.
The software has moved into its private beta phase, with OpenAI planning to offer a cloud-based commercial subscription later this year. This move marks a significant stride toward integrating GPT models into business applications, bringing us one step closer to the AGI GPT reality.
The Marvel of GPT-3: A Milestone in AGI Evolution?
GPT-3 is a machine-learning model with an impressive 175 billion parameters, making it capable of generating astonishingly human-like text. It’s been applied to numerous tasks, from generating short stories to even coding HTML. These capabilities have been turning heads and inciting discussions around AGI GPT models. But is it all it’s cracked up to be?
GPT-3’s predecessor, GPT-2, laid the foundation for the current model. While the underlying technology hasn’t changed much, what distinguishes GPT-3 is its sheer size—175 billion parameters compared to other language models like T5, which has 11 billion parameters. This scale is a result of extensive training on data largely sourced from the internet, enabling GPT-3 to reach or even surpass current State-Of-The-Art benchmarks in various tasks.
The Limitations and Weaknesses
Despite its staggering capabilities, the GPT-3 model is not without its flaws. Despite its human-like text generation capabilities, GPT-3 is still prone to generating hateful, sexist, and racist language. It’s a powerful tool but lacks the genuine smarts and depth that come with human cognition. In essence, while the output may look human-like, it often reads more like a well-crafted collage of internet snippets than original thought.
Most people tend to share positive examples that fit their bias towards the machine’s language „understanding.“ However, the negative implications, such as the generation of offensive or harmful content, need to be considered seriously. For example, GPT-3 has been found to generate racist stories when prompted with specific inputs, which raises concerns about the technology potentially doing more harm than good.
Not Quite AGI
Many have been quick to label GPT-3 as a stepping stone towards AGI. However, this might be an overestimation. GPT-3 can make glaring errors that reveal a lack of common sense, a key element in genuine intelligence. As OpenAI co-founder Sam Altman notes:
„AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.“
Sam Altman, CEO, OpenAI
The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.
My upcoming book Decisively Digital devotes an entire chapter to the role of GPT-3 in business and its potential to serve as a stepping stone toward AGI. From automating customer service to generating insightful reports, GPT-3 offers a wealth of opportunities for enterprises. However, the book also delves into the ethical considerations and potential pitfalls of adopting this powerful technology.
Concluding Thoughts: AGI GPT—A Long Road Ahead
While GPT-3 serves as an intriguing glimpse into the future of AGI, it is just that—a glimpse. We have a long road ahead in the quest for AGI GPT models that can mimic true human intelligence. As we navigate this fascinating journey, a balanced perspective is crucial.
To stay updated on these critical topics and much more, connect with me on Twitter and LinkedIn, and be on the lookout for the release of Decisively Digital.
AI has to potential to save millions of lives by applying complex algorithms | Photo Credit: via Brother UK
Good health is a fundamental need for all of us. Hence, it’s no surprise that the total market size of healthcare is huge. Developed countries typically spend between 9% and 14% of their total GDP on healthcare.
The digital transformation in the healthcare sector is still in its early stages. A prominent example is the Electronic Health Record (EHR) in particular, and, in general poor quality of data. Other obstacles include data privacy concerns, risk of bias, lack of transparency, as well as legal and regulatory risks. Although all these matters have to be addressed in a Digital Strategy, the implementation of Artificial Intelligence (AI) should not hesitate!
AI has to potential to save millions of lives by applying complex algorithms to emulate human cognition in the analysis of complicated medical data. AI furthermore simplifies the lives of patients, doctors, and hospital administrators by performing or supporting tasks that are typically done by humans, but more efficiently, more quickly and at a fraction of the cost. The applications for AI in healthcare are wide-ranging. Whether it’s being used to discover links between genetic codes, to power surgical robots or even to maximize hospital efficiency, AI is reinventing modern healthcare through machines that can predict, comprehend, learn and act.
Let’s have a look at ten of the most straightforward use cases for AI in healthcare that should be considered for any Digital Strategy:
1. Predictive Care Guidance:
AI can mine demographic, geographic, laboratory and doctor visits, and historic claims data to predict an individual patient’s likelihood of developing a condition. Using this data predictive models can suggest the best possible treatment regimens and success rate of certain procedures.
2. Medical Image Intelligence:
AI brings in advanced insights into the medical imagery specifically the radiological images. Using AI providers can gain insights and conduct automatic, quantitative analysis such as identification of tumors, fast radiotherapy planning, precise surgery planning, and navigation, etc.
3. Behavior Analytics:
AI helps to solve patient registry mapping issues for and help the Human Genome Project map complicated genomic sequences to identify the link to diseases like Alzheimer’s.
4. Virtual Nursing Assistants:
Conversational-AI-powered nurse assistants can provide support patients and deliver answers with a 24/7 availability. Mobile apps keep the patients and healthcare providers connected between visits. Such AI-powered apps are also able to detect certain patterns and alert a doctor or medical staff.
5. Research and Innovation:
AI helps to identify patterns in treatments such as what treatments are better suited and efficient for certain patient demography, and this can be used to develop innovative care techniques. Deep Learning can be used to classify large amounts of research data that is available in the community at large and develop meaningful reports that can be easily consumed.
6. Population Health:
AI helps to learn why and when something happened, and then predict when it will happen again. Machine Learning (ML) applied to large data sets will help healthcare organizations find trends in their patients and populations to see adverse events such as heart attacks coming.
7. Readmissions Management:
By analyzing the historical data and the treatment data, AI models can predict, flag the causes of readmissions, patterns, etc. This can be used to reduce the hospital readmission rates and for better regulatory compliance by developing mitigating strategies for the identified causes.
8. Staffing Management:
Predictive models can be developed by analyzing various factors such as historical demand, seasonality, weather conditions, disease outbreak, etc. to forecast the demand for health care services at any given point of time. This would enable better staff management and resource planning.
9. Claims Management:
AI detects any aberrations such as – duplicate claims, policy exceptions, fictitious claims or fraud. Machine learning algorithms recognize patterns in data looking at trends, non-conformance to Benford’s law, etc. to flag suspicious claims.
10. Cost Management:
AI automates the cost management through RPA, cognitive services, which will help in faster cost adjudication. It will also enable analysis, optimization, and detection by identifying patterns in cost and flagging any anomalies.
Conclusion:
As these examples show, the wide range of possible AI use cases can improve healthcare quality and healthcare access while addressing the massive cost pressure in the healthcare sector. Strategic sequencing of use cases is mandatory to avoid implementation bottlenecks due to the scarcity of specialized talent.
Which use cases for AI in healthcare would you add to this list?
Share your favorite AI use case in the blog post comments or reply to this tweet:
Darren Cooper and I had the pleasure to welcome 200 Data & AI enthusiasts! Furthermore, we were happy to announce that our Data & AI Meetup group has 1,070 members and our brand new Data & AI LinkedIn group already has 580 members.
Reinforcement Learning of Train Dispatching at Deutsche Bahn
Dr. Tobias Keller, Data Scientist at DB Systel, showed in his session how Deutsche Bahn aims at increasing the speed of the suburban railway system in Stuttgart (S-Bahn) using Artificial Intelligence. In particular, a simulation-based reinforcement learning approach provides promising first results.
First Speaker at tbe Data & AI Hub Reinforcement Learning of Train Dispatching at Deutsche Bahn Dr. Tobias Keller, Data Scientist @dbsystelpic.twitter.com/nPzrcTgHgr
Sascha Dittmann, Cloud Solution Architect for Advanced Analytics & AI at Microsoft, showed in his presentation, how TensorFlow and other ML frameworks can be used better in a team through appropriate Microsoft Cloud services. He presented different ways of how data science experiments can be documented and shared in a team. He also covered topics such as versioning of the ML models, as well as the operationalization of the models in production.
Visual Analytics: from messy data to insightful visualization
Daniel Weikert, Expert Consultant at SIEGER Consulting, showed in his session the ease of use of Microsoft Power BI Desktop. He briefly highlighted the AI Capabilities which Power BI provides and showed a way on how to get started with messy data, doing data cleaning and visualize results in an appealing way to your audience.
15th Data & AI Meetup in Frankfurt: Visual Analytics: from messy data to insightful visualization by Daniel Weikert @sgc_siegerpic.twitter.com/sTvaAwQxc2
If you’ve dreamed of sharing your Data & AI story with many like-minded Data & AI enthusiasts, please submit your session proposal or reply to the recap tweet:
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