Mediaboss Marketing

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.

Looking for real-world insights into artificial intelligence and hyperautomation? Subscribe to the AI and Hyperautomation channel:

Read the original:
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.

Read the original:
Artificial Intelligence And Machine Learning Will Power The Digital ... - CIOReview

May 3, 2023 by Diana Ambolis

170

Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and

Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help inform investment decisions and maximize returns.

Machine learning algorithms can be trained to analyze a range of data points and variables that are relevant to NFT value. This could include factors such as the artists reputation, the rarity of the NFT, the size of the NFT market, and even social media sentiment around a particular NFT. By analyzing this data, machine learning algorithms can identify patterns and correlations that can be used to predict the future value of a given NFT.

Determining the true value of an NFT can be challenging, with many factors to consider, including the artists reputation, the rarity of the NFT, and social media sentiment around a particular NFT. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help determine the value of NFTs. In this article, well explore the top 10 benefits of using machine learning to determine NFT value.

Machine learning offers a range of benefits for investors seeking to determine NFT value. By providing accurate predictions, improving efficiency, and reducing bias, machine learning can help investors make more informed decisions about NFT investments. As the NFT market continues to evolve, it is likely that machine learning will become an increasingly important tool for investors seeking to capitalize on this emerging market.

Also, read The Top 5 Best NFT Products So Far: A Closer Look

One of the key benefits of using machine learning for NFT investment is that it can help investors make more informed decisions about which NFTs to buy or sell. By providing insights and predictions about future value, machine learning algorithms can help investors identify undervalued NFTs that have strong potential for growth, as well as overvalued NFTs that may be at risk of declining in value.

Another benefit of using machine learning for NFT investment is that it can help investors manage risk. By providing predictive models and insights, machine learning algorithms can help investors understand the potential risks and rewards associated with a given NFT investment, allowing them to make more informed decisions about how to allocate their resources.

There are also potential drawbacks to using machine learning for NFT investment. For example, the accuracy of predictive models can be influenced by a range of factors, including the quality and quantity of data used to train the algorithm. In addition, the NFT market is still relatively new and untested, making it difficult to predict how the market will behave over time.

Despite these potential drawbacks, many investors are turning to machine learning as a way to inform their NFT investment decisions. As the NFT market continues to grow and evolve, machine learning is likely to become an increasingly important tool for investors seeking to capitalize on this emerging market.

Machine learning has the potential to revolutionize the world of NFT investment, providing investors with new insights and predictive models that can inform investment decisions and maximize returns. By analyzing a range of data points and variables, machine learning algorithms can identify patterns and correlations that can be used to predict NFT value and manage risk. While there are potential drawbacks to using machine learning in this context, the benefits are significant, and it is likely that this technology will become an increasingly important tool for investors seeking to capitalize on the emerging NFT market.

See the article here:
Machine Learning And NFT Investment: Predicting NFT Value And ... - Blockchain Magazine

The recent advent of ChatGPT has created an explosion of interest in Artificial Intelligence (AI) and Machine Learning (ML). While everyone is theorising about the potential use of these technologies, AI and ML already accelerate identity security by streamlining processes and providing actionable insights to administrators and users.

Identity security refers to the measures and techniques used to protect an individuals or machines unique identity and sensitive information from being stolen, misused, or compromised. This type of security focuses on verifying and authenticating the identity of a human or digital user before granting access to certain systems or information. It involves several components, including authentication, authorisation, and access control.

Securing identities is critical in todays digital age as cyber threats continue to evolve and the risks associated with data breaches and identity theft become increasingly severe. Organisations and individuals must proactively protect their personal identity and sensitive information, including implementing strong authentication mechanisms, regularly monitoring and auditing access controls, and staying up-to-date with the latest security best practices and technologies.

Before looking into how Artificial Intelligence and Machine Learning benefit in bolstering identity security programmes, lets establish how AI and ML function and the main differentiators of these two technologies.

While AI and ML are both fields of computer science that deal with developing intelligent systems, theres a significant difference between these two technologies.

AI involves creating computer programmes that can perform tasks that typically require human intelligence, such as problem-solving, decision-making, and natural language processing. ML is a subfield of AI that creates algorithms which can learn and improve from data without being explicitly programmed.

The main difference between these two technologies is that AI is a broader concept encompassing different techniques and approaches. At the same time, ML is a specific application of AI that involves training algorithms to recognise patterns in data and make predictions or decisions based on that data.

Next to academic or theoretical AI research, which focuses on developing new algorithms or advancing the fields fundamental knowledge, applied and generative AI are the two branches that find practical application in day-to-day life, professional or personal.

Applied AI solutions often involve natural language processing, computer vision, or other AI techniques combined with domain-specific expertise and data. This branch is used in various fields, such as healthcare, finance, transportation, manufacturing, etc. ML falls under this branch of AI technology.

Examples of applied AI solutions include fraud detection in financial transactions, predictive maintenance in manufacturing, chatbots for customer service, recommendation systems for e-commerce, and image recognition in healthcare.

Overall, applied AI aims to bring the benefits of AI technologies to practical use cases, improving efficiency, productivity and decision-making in various industries and domains.

shutterstock/Blue Planet Studio

On the other hand, general AI refers to systems that can perform human-like tasks. It is a subset of machine learning that involves training models to generate novel outputs, such as images, videos, music or text.

Using deep learning algorithms to learn patterns and relationships within a dataset, generative AI can create new content similar in style, format, or structure. To work, these algorithms are trained on large datasets, often containing millions of examples, and can produce highly realistic and convincing outputs, as we currently observed with ChatGPT.

Generative AI has potential applications in areas such as healthcare, finance, and autonomous driving, where it can be used to generate synthetic data for testing and training AI models.

Drilling down to identity security, it is ML which can be most readily leveraged to analyse user behaviour, find and mitigate vulnerabilities, and streamline operations.

ML technology can provide valuable insights and suggestions based on data analysis, optimising workflows and reducing frustration for administrators tasked with managing identity security programmes.

There are multiple ways in which ML can be effectively applied to this field, for example, by empowering workforces, simplifying management, reducing costs, and more. With its contextual understanding, a system can automatically recommend the next step or revise workflows, leading to improved and streamlined processes, fewer human errors, and stronger overall security.

One instance of how ML benefits identity security is when evaluating access rights and usage patterns. Here, ML enables the system to recommend access throughout an identitys lifecycle, from the initial request to ongoing micro-certification campaigns.

Furthermore, many of the routine activities related to identity security can be automated, making employee onboarding faster. The system can also offer insights to entitlement owners on how a persons access compares to that of their peers and other roles, helping expedite approvals and minimise digital exhaustion for administrators and end-users.

Moreover, machine learning can detect unusual behaviour and identity anomalies that may threaten the organisation. By analysing these outliers, access revocations can be automated or used to initiate additional reviews. When developing and maintaining roles, ML can evaluate current roles, identify any similar ones that could be merged, and suggest new roles that may be advantageous.

Using analytics, AI and ML to improve enterprise identity security is critical to outpace cybersecurity threats. Rather than buzzwords, leaders want to see real-world use cases where human and machine intelligence meaningfully converge.

AI can bring several benefits to Identity Access Management (IAM), such as:

AI and ML have the potential to revolutionise identity security and speed up the adoption of related programmes by providing actionable insights and streamlining processes.

Identity security is critical in todays digital age, where cyber threats continue to evolve, and the risks associated with data breaches and identity theft become increasingly severe.

ML can automate routine activities related to identity security, detect unusual behaviour and identity anomalies, evaluate access rights and usage patterns, and offer insights to entitlement owners.

Additionally, AI algorithms can enhance security measures and enhance user experience by reducing the time and effort required to manage IAM programmes. Utilising these capabilities, organisations can quickly identify and address high-risk access and activities, ensuring regulatory compliance on an ongoing basis.

Integrating AI and ML in identity security programmes can improve efficiency, productivity, and decision-making, enabling organisations and individuals to protect their personal identity and sensitive information. Moreover, organisations can shrink their threat landscape by reducing over-privileging and human error.

Jonathan NealVP, Solutions EngineeringSaviynt

Continue reading here:
How can AI and Machine Learning protect identity security? - Innovation News Network

Remember tags? Around2007, I was all overthem, and I feel no shameabout that. Well, notmuch shame. [Davidsbook, Everything Is Miscellaneous,was published in 2007. Ed.] Lettingusers apply whatever tags, or folksonomies,they wanted to digital contentblew apart constraints on knowledgethat we'd assumed for millennia were strengths of knowledge. In fact, the idea that each thing had only one real tag was the bedrock of knowledgefor thousands of years: A tomato is a vegetable, not some other thing.

Ok, nerds, youreright; a tomato is actuallya berry. But youre justproving my point: We liketo think that a thing is one thing and notany another. At least in some contexts.

Of course, before tags, we would applymultiple classifications to things: A bookabout tomatoes might get classified underrecipes, healthy foods, and the genusSolanum. But a tomato is also a classicallyred object, roundish, delicious, squishy, asource of juice, a bad thing to learn jugglingwith, something we used to throwat bad actors and corrupt politicians, andso much more.

Then, with sites that allowed userbasedtagging, users could tag tomatoeswith whatever attributes were importantto the user at that time. We can now dothis with the photos we take, the placeswe go on our maps, the applications weuse, the sites we visit, the music we listento. Tags have become so commonthat theyve faded from consciousnesssince 2007, although sometimes a cleverhashtag pops up.

While AI in the form of machine learningcan automatically apply tags, it mayreduce the need for tags. Already we cansearch for photos based on their content,colors, or even their mood and all withoutanyone attaching tags to them.

Machine learning redefinestagging

But more may be at stake. Mightmachine learning complete the conceptualjob that tagging began, leading us from adefinitional understanding of what thingsare to a highly relational view? My prediction(My motto: Someday Ill get oneright!) is that within the next few years,dimensionality is going to become animportant, everyday word.

One view of meaning is that a wordis what its definition saysit is, as if a definition werethe long way of saying whatthe word says more compactly.But thats not howwe use or hear words. InThe Empire Strikes Back,when Princess Leia says, Ilove you to Han Solo andhe replies, I know, thedefinitions of those wordscompletely miss what justtranspired.

Tagging has made clear that thingshave very different meanings in differentcontexts and to differentpeople. Definitions havetheir uses, but the timeswhen you need a dictionaryare the exception. Tags makeexplicit that what a thing is(or means) is dependent oncontext and intention.

Machine learning is gettingus further accustomedto this idea, and not just for words. Forexample, a medical diagnostic machinelearning model may have been trained onhealth records that have a wide variety ofdata in them, such as a patient's heart rate and blood pressure, weight, age, cholesterollevel, medicines theyre taking, past history, location, diet, and so forth. The more factors, the more dimensions.

Link:
Tags, AI, and dimensions - KMWorld Magazine