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Artificial intelligence has come a long way since games like Akinator started terrifying people on the internet in the mid-2000s. Thankfully, AI’s progression hasn’t gone in a direction that would see a real-world equivalent to the Geth rising up. Today, AI is used in many practical settings — like in our virtual assistants and in our cars — as well as in more creative settings, such as music production, writing, and yes, even NFTs.
In the NFT space, AI-adjacent technologies have been used in generative art projects like Art Blocks. Herein, users can browse the site’s marketplace for styles they like, purchase them, and receive generative art derived from that style in return. But what of NFT art billed specifically as created by an AI?
Is an AI an artist or a tool?In the past few months, one specific creative application of AI technology has gone viral: image creation. Training AI models to turn text-based prompts into images is nothing new. But we’ve recently seen images generated via OpenAI’s DALL-E program flood our feeds as such AI-based tools become more user-friendly.
According to OpenAI’s website, DALL-E works by using its vast language database to turn text-based inputs from users into easily recognizable images matching or approximating the text input. This allows DALL-E to recognize parameters within the text-based input, such as the spatial relationships of the objects specified in the input as well as the individual properties of objects included in the prompt. Interestingly enough, nearly every pop culture character you can think of is included in DALL-E’s data banks.
Source: DALL-E mini
Many NFT collections (particularly PFP NFT collections) include thousands of unique images. As such, generating NFTs via AI tools like DALL-E seems like a no-brainer with regard to maximizing efficiency. However, as compelling as it may seem to feed absurd prompts into DALL-E and transform the output into NFTs, the platform raises some ownership-related questions for those hoping to utilize this technology to create an NFT collection.
Despite the fact DALL-E grants users a creative commons license to use images generated via the AI “however they please,” copyright law on works generated by non-human entities is still rather murky. Let’s take a look at the famous “monkey selfies” copyright dispute as an example.
In 2011, wildlife photographer David Slater engineered a situation where a community of crested macaques in Indonesia was able to take “selfies” with his camera equipment. While Slater claims ownership of the photos, the U.S. Copyright Office states that works by non-humans are not eligible to be copyrighted. Thus, the photos ended up in the public domain.
Source: David Slater via Wikimedia Commons
So where does this leave work created by AI? AI-created work seems to be more or less in the same boat as animal-made art, as it lacks the “human authorship” needed to bestow rights intended to protect the artist.
Humans in the loopDoes this mean that any art generated by an AI would prove to be an unsellable product? Far from it. It all depends on how the human creators of a project package it.
For example, Untitled Frontier utilized AI and machine learning models to build out pieces for its NFT collections, but these tools supplemented work wholly made by humans. With this technology at their disposal, Untitled Frontier was able to create a way for writers to sell NFT merchandise of their work by creating pieces of art inspired by short stories in their ongoing series.
“At the end of the day, the ‘machine’ still has to be guided by a human: whether that’s for specific outputs, or for inspiration,” said founder Simon de la Rouviere in an interview with nft now. “Ultimately, the creator still has to choose what eventually goes onto the proverbial canvas or manuscript.
Source: Botto
Keeping humans actively involved in these AI-driven projects seems to be a re-occurring theme within the space. This is evidenced by projects like Botto, which relies on consensus — as well as Botto’s built-in capacity for learning and self-improvement — to steer the direction of the AI artist’s output via community feedback. This culminates in the minting and sale of a single NFT that’s gone through considerable amounts of scrutiny by the very human members of the Botto community.
Despite the massive strides AI-powered projects have made in the art world, particularly in the NFT space some members of the AI enthusiast community speculate whether the AIs are actually creating anything in the first place. “AI agents are not creating art; rather, they are replicating art,” wrote Will Chambers in an article on Towards Data Science. Herein, he detailed how researchers were able to develop a Generative Adversarial Network (GAN), “a type of artificial intelligence algorithm in which two neural nets play off against each other to […] generate works of art,” Chambers said.
These GANs, also known as Creative Adversarial Networks (CANs), Chambers argues, don’t make capital-A “Art” in the way that humans do. They may be able to learn and create, but the resulting work lacks the essential human component that separates craft from art. “When a CAN agent generates a new image, it is not drawing upon its personal or collective experiences, neither conscious nor unconscious,” he said. “Its generated images are predicated on human experiences, as manifest in the symbols and archetypes captured in our human artwork on which the CAN agent is conditioned and trained.”
The future of AI in NFTsBut is art the only application of AI we could be seeing in the NFT landscape moving forward? Just as NFTs encompass far more than art, so too does the use of AI within the NFT sphere.
For example, we have projects like Alethea and Altered State Machine that allow users to embed their existing NFTs with an AI. According to their respective websites, doing so grants a user’s NFT the capacity to learn and grow over time, as well as providing users with more meaningful ways to interact with their NFT via the metaverse. Additionally, these AI-embedded NFTs are touted as appreciating in value not just in response to the market, but also thanks to how far along said NFT is in its journey of evolution, learning, and self-amendment.
With AI, budding artists, creators, and anyone else hoping to make a dent in the NFT space and beyond don’t have access to a tool that’s simply just going to work for them, but with them. As de la Rouviere put it in a post on his blog, “The dream of an autonomous artist is exciting because it mediates a conversation between us and technology. To make art, autonomous, feels like creating life: a machine in the aether that is trying to tell us something. We become symbiotic, like bacteria in our biological bodies, in creating a form of life that talks to itself, and to us through art.”
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Why Uwerx (Werx) Surpasses Arbitrum (Arb) As The Next Big Thing In Cryptocurrency Investing
As the crypto industry evolves rapidly, new projects emerge, each vying for their place in the spotlight. Among these contenders are Uwerx and Arbitrum (ARB), two innovative platforms that offer promising investment opportunities.
Arbitrum (ARB): A Formidable Layer-2 SolutionsBorn in August 2023, Arbitrum (ARB) has cemented its longevity and emerged as a dominant force. Its recent token launch ignited a spark, propelling it to the forefront of daily trading volumes—this surge in activity hints at the potential for a price surge during bullish market conditions.
Unveiling an airdrop event that has shaken the crypto market, Arbitrum (ARB) rewards its dedicated community members with a jaw-dropping opportunity to claim up to $12,000 for their prior network usage. While no presale event was held for Arbitrum (ARB), an equally exciting chance awaits with Uwerx.
As the journey continues for Arbitrum (ARB), our innovative algorithm forecasts an impressive 2024 outlook. Anticipate a maximum price of $1.12 while the average price for the year stabilizes around $2.87. Even in the face of a bear market, Arbitrum (ARB)’s resilience shines through, with a projected minimum price of $2.53 in 2024.
Uwerx (WERX): Power to the CommunityUwerx is the groundbreaking freelancing platform set to unleash a wave of innovation. Uwerx sets itself apart by charging a mere 1% transaction fee, leaving competitors like Upwork and Fiverr in the dust with their hefty 10% and 20% fees, respectively. By prioritizing freelancers’ earnings, Uwerx redefines the rules, giving them the financial freedom they deserve.
As the project prepares for its debut on centralized exchanges, the Uwerx team is diligently working behind the scenes. The contracts will be renounced once the project is ready for launch and taxes have been reduced to zero. This move guarantees the project’s transparency and independence, solidifying Uwerx as a force to be reckoned with in the freelancing industry.
With a resounding 98.2% in favor, the community has spoken loud and clear. In response to their trust, Uwerx is locking its tokens for an impressive 25 years, ensuring top-notch security and setting a new industry standard.
Get ready for a revolution as Uwerx unveils its Alpha platform. Uwerx is making remarkable strides in the development of its Alpha platform, and exciting updates are on the horizon. On May 19th, Uwerx released the PDF version of the Alpha Platform, giving users a sneak peek at what’s in store.
The grand finale is scheduled for August 4th, when Uwerx will unleash its full potential, empowering freelancers to reach new heights.
Uwerx has more exciting releases planned for the coming weeks. They’ll be unveiling additional parts of the Alpha platform, including the login Page, User Dashboard, Settings, Posting Jobs, and Finding Jobs. It’s a comprehensive platform that aims to cater to all your freelance needs.
What’s even better? Uwerx is preparing for the transition from Alpha to Beta, where everyone will have the opportunity to personally test the platform. They value your feedback, thoughts, and recommendations. If you have any design suggestions, don’t hesitate to share them by sending an email to [email protected].
With over 5,000 sign-ups and counting, Uwerx is a testament to freelancers’ passion and enthusiasm worldwide. The Uwerx team listens to their community, adapting token allocations to meet their desires and fostering a supportive environment where every freelancer’s voice is valued.
Uwerx (WERX): Ascending to New HeightsBuilt on trust and meticulous attention to detail, Uwerx is a security fortress. Audited by industry leaders like SolidProof and InterFi Network, Uwerx ensures that freelancers can focus on their craft without worrying about their digital assets.
Uwerx’s rise to prominence includes listings on CoinSniper and the highly anticipated Uniswap listing by August 1st. These strategic moves boost accessibility and liquidity, creating a thriving freelancer ecosystem.
Uwerx continues to thrive and expand, demonstrating its commitment to excellence. The introduction of the Uwerx Vault presents an exciting opportunity for users to stake their WERX tokens, earning rewards based on platform variables akin to the popular concept of staking. This feature adds a layer of engagement and potential benefits for the community.
The presale journey of Uwerx has been remarkable, with Stage 1 completing in just 17 days and Stage 2 in a mere 8 days. Currently, in Stage 5, Uwerx offers its tokens at a rate of $0.041 per token, with a generous 15% bonus on purchases, providing investors with a unique opportunity to participate in the project’s growth.
As the hard-cap presale end date of July 31st approaches, Uwerx prepares to reshape the freelancing landscape. Brace yourself for a revolution of empowerment and limitless possibilities. Uwerx is here to redefine freelancing and leave a lasting mark.
Uwerx (WERX) Price Prediction?The future looks bright for Uwerx, and experts predict a positive run for the WERX tokens. We are confident that by Q3-Q4 2023, WERX tokens will sell for $0.88, and this price will increase to $2.37 by Q1-Q2 2024.
While Arbitrum (ARB) is a good crypto, these features and presale price set Uwerx apart as a more lucrative investment opportunity.
Presale: invest.uwerx.network
Artificial Intelligence Jobs In 2023
Artificial intelligence jobs are not a new phenomenon, but the AI job market is growing as AI market itself is seeing rapid expansion. According to research firm IDC, AI is currently seeing an annual growth rate approach 40 percent.
Amid these changes, AI job titles have changed and expanded – and AI paychecks are heading skyward. Earlier roles were “statistician” or “mathematician,” while today you’ll hear newer terms like “data scientist” and “predictive analytics expert.”
The rapid emergence of new AI titles reflects the fact that AI has become practical for mainstream use as the result of affordable cloud computing and storage costs, a change from prohibitively expensive supercomputers. The growth of AI companies and the expansion of related technology like machine learning has expanded AI job titles.
Similarly, some of the AI algorithms used today have existed for several decades, but until recently, they lacked the volume and richness of data required to drive value. Now there are many different types of AI jobs or roles available, some of which merely add “AI” to an existing title (e.g., “AI developer”). Others reflect different aspects of AI (e.g., “data engineer,” “algorithm developer” or “machine learning scientist”).
For the purpose of this article, AI job titles, their general job descriptions and their salary ranges are limited to technical titles.
Also see: The Pros and Cons of AI
A typical ad for an AI job in today’s rapid growth artificial intelligence sector.
Jobseekers and career builders are wise not to take AI job titles at face value. For example, some companies require data scientist candidates to hold a PhD or MS in Computer Science or Statistics, while other organizations may accept a BS or even no college degree and certain types of experience. Similarly, a “senior” title may require a graduate degree and more experience at some companies than others.
Some of today’s most popular AI job titles include:
AI developer
AI engineer
Algorithm developer
AWS machine learning engineer
Azure data scientist
Data scientist
Lead data engineer
Lead data scientist
Machine learning scientist
ML data developer
Senior data scientist
Senior ML engineer
It is important to read the mandatory requirements of any job listing to determine whether it is actually a fit. For example, some technical AI positions require R and Python expertise whereas others may only require R or Python experience.
Another reason to maintain an open mind about job descriptions is that the people writing and approving job requisitions may lack the technical expertise to articulate what the organization actually needs. The result is sometimes unrealistic qualifications that even the most sought-after experts lack.
Following are some sample job descriptions that are based on actual job postings. They have been abbreviated for easier comparison.
Job description: Responsible for maintaining, enhancing and implementing AI solutions.
Mandatory skills:
R, Python and/or C#
AWS or Azure AI services and frameworks
AI, ML, NLP, REST APIs, libraries, frameworks
Cloud application design, development and deployment
Hands-on experience with AI, ML, NLP and cloud applications
Education: BS, Computer Science
Job description: Develops solutions to large-scale problems and bridges the gap between software developers and research scientists
Mandatory skills:
ML, DL, NLP, computer vision
Java
Past experience defining and coding and validation tests
Test automation script writing
Programming experience
Education: MS or PhD in Computer Science, AI, ML or related field.
Job description: Develops algorithms for specific use cases.
Mandatory skills:
Algorithm development
Algorithm performance assessment and reporting
Algorithm optimization
Education: BS in Computer Science or equivalent as relevant to the position (e.g., Robotics or Electrical Engineering)
Job description: Build out data models and create very large data sets.
Mandatory skills:
3+ years of AWS or Azure services experience
3+ years ML and data labeling
3+ years Python or R and Python
3 – 5 years data management experience
Education: BS, Compute Science, MS or PhD may be preferred
Job description: Translate customer requirements into POCs and successful solutions.
Mandatory skills:
R and Python
Hadoop
2+ years of data science experience
ML, NLP, DL, depending on the position and clients
Experience with platform (AWS or Azure) and its services
Education: MS in Computer Science or related field
Job description: Organize structured and unstructured data, build data models and interpret complex data sets
Mandatory skills:
R and/or Python
ML and/or NLP, DL depending on the position
Data mining
Education: BS or MS in Computer Science or Equivalent
Job description: Re-engineer business intelligence processes, design and develop data models, and share your expertise throughout the deployment process.
Mandatory skills:
AWS or Azure
Cloud computing
Hadoop, Spark
Python, Scala or Java
ML model deployment
Education: BS, Computer Science or equivalent
Job description: Turn client challenges and goals into successful AI, ML or DL solutions that meet or exceed expectations; train junior data scientists.
Mandatory skills:
R, Python, or both preferred
POC development and demonstration
8+ years end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables and presentations
8+ years of development, implementation and use of ML models and quantitative techniques for prediction and classification
2+ years of relevant, specialized experienced (e.g., NLP)
Education: MS in Computer Science, Statistics, or Math; PhD preferred
Mandatory skills:
Proven, in-depth understanding of ML algorithms and modeling including supervised, unsupervised and reinforcement learning models, transfer learning, optimization and probabilistic graphical models
In-depth experience with Spark or Hadoop and either PyTorch or Tensorflow
Python and Java, Scala, and/or R
Experience creating production environment data analytics and applications
Education: PhD in Computer Science or related field with a focus on ML, AI or data mining or an MS plus experience equivalent to holding a PhD
For any number of complex reasons, some AI jobs require additional significant experience, as do the positions below. In truth, the field is still new enough that the exact amount of experience is heavily dependent on individual employers.
Job description: Apply different NLP techniques to areas such as classification, data/knowledge extraction, disambiguation, sentiment analysis, etc. Identify and categorize entities in text such as people, places, organizations, date/time, quantities, percentages, currencies and more.
Mandatory skills:
R, Python and/or C#
AWS or Azure AI services and frameworks (cognitive, bots)
AI, ML, NLP, REST APIs, libraries, frameworks
Cloud application design, development and deployment
Experience Level:
10+ years with the target platform (AWS or Azure)
5+ years development experience
Hands-on POC experience proving an architecture concept
Experience with large, diverse data sets
Education: MS in Computer Science or Computer Information Systems (CIS)
Job description: Responsible for maintaining, enhancing and implementing solutions
Mandatory skills:
R, Python and/or C#
AWS or Azure AI services and frameworks (cognitive, bots)
AI, ML, NLP, REST APIs, libraries, frameworks
Cloud application design, development and deployment
Experience Level:
10+ years software development
7+ years R, Python or C#
5+ years AI, ML, NLP and cloud application development
Education: BS, Computer Science or Engineering
Job description: Design and implement cutting-edge solutions across a breadth of domain areas.
Mandatory skills:
ML, DL, NLP, computer vision, recommendation engines, pattern recognition, large-scale data mining
Predictive, statistical, and data mining modeling
Graph databases
AWS, Azure or IBM Watson
Hadoop, Spark or other Big Data platform
Proficient with ML algorithms
Education: BS or MS in Computer Science, Engineering, Statistics or Math
AI-related jobs tend to share very similar compensation ranges, which may be misleading. While it may seem odd that people with the same title could have base compensation that varies by $50,000 or $100,000, some of the differences have to do with the present levels of compensation in specific markets. For example, jobs in San Francisco and Manhattan tend to pay more than those in other major metropolitan areas given the cost-of-living differences.
Job candidates should therefore go into a job search with an open mind and research what’s available at that point in time. The candidate should also be clear about which titles and compensation packages are acceptable since a better title may not mean more pay or vice versa.
Current salary ranges for the positions discussed above are:
AI developer: $90,000 – $150,000
AI engineer – $130,000 – $210,000
Algorithm developer – $90,000 – $130,000
Machine learning engineer – $70,000 – $170,000
Azure or AWS data scientist – $85,000 – $160,000
Data scientist: $50,000 – $150,000
Lead data engineer: $90,000 – $175,000
Lead data scientist: $125,000 – $195,000
Machine learning scientist: $100,000 – $150,000
ML data developer: $100,000 – $170,000
Senior AI developer: $90,000 – $150,000 (high end)
Senior data engineer: $90,000 – 175,000
Senior data scientist: $95,000 – $210,000
Artificial Intelligence Is The Transformative Force In Healthcare
Artificial intelligence, the technology that is seen as a home name today is poised to become a transformational force in healthcare. Healthcare industry is where a lot of challenges are encountered and opportunities open up. Starting from chronic diseases and radiology to cancer and risk assessment, artificial intelligence has shown its power by deploying precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. The complexity and rise of data in healthcare have unveiled several types of artificial intelligence. Today, artificial intelligence and robotics have evolved to the stage where they can take better care of patients better than medical staff and human caretakers. The global artificial intelligence in the healthcare market is expected to grow from US$4.9 billion in 2023 and reach US$45.2 billion by 2026 with a projected CAGR of 44.9% during the forecast period. Artificial intelligence and relevant technologies are prevalent in business and society and are rapidly moving into the healthcare sector. One thing that makes them stand out from manual labor is that technology has the potential to transform many aspects of patient care, as well as administrative processes within providers, payers, and pharmaceutical organizations. Today, algorithms are already outperforming radiologists at spotting malignant tumors, and guiding researchers in how to construct cohorts for costly clinical trials. Even though artificial intelligence in healthcare has evolved so much over a countable number of years, humans still believe that we have a long way to go before AI replaces humans for broad medical process domains. Most artificial intelligence and healthcare technologies have strong relevance to the healthcare field, but the tactics they support can vary significantly. Using computers to communicate with humans, especially ill humans, is not a new idea by any means. For many years now, doctors and other medical staff have been availing disruptive trends in one way or the other. But what has changed now is the disappearance of human intervention. Technologies of the 21st century are directly interfacing with humans without the help of keyboards, mice, and monitors. In a significant way, artificial intelligence has simplified the lives of patients, doctors, and hospital administrators by taking over many time-consuming and labor-intense jobs.
Artificial intelligence, the technology that is seen as a home name today is poised to become a transformational force in healthcare. Healthcare industry is where a lot of challenges are encountered and opportunities open up. Starting from chronic diseases and radiology to cancer and risk assessment, artificial intelligence has shown its power by deploying precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. The complexity and rise of data in healthcare have unveiled several types of artificial intelligence. Today, artificial intelligence and robotics have evolved to the stage where they can take better care of patients better than medical staff and human caretakers. The global artificial intelligence in the healthcare market is expected to grow from US$4.9 billion in 2023 and reach US$45.2 billion by 2026 with a projected CAGR of 44.9% during the forecast period. Artificial intelligence and relevant technologies are prevalent in business and society and are rapidly moving into the healthcare sector. One thing that makes them stand out from manual labor is that technology has the potential to transform many aspects of patient care, as well as administrative processes within providers, payers, and pharmaceutical organizations. Today, algorithms are already outperforming radiologists at spotting malignant tumors, and guiding researchers in how to construct cohorts for costly clinical trials. Even though artificial intelligence in healthcare has evolved so much over a countable number of years, humans still believe that we have a long way to go before AI replaces humans for broad medical process domains. Most artificial intelligence and healthcare technologies have strong relevance to the healthcare field, but the tactics they support can vary significantly. Using computers to communicate with humans, especially ill humans, is not a new idea by any means. For many years now, doctors and other medical staff have been availing disruptive trends in one way or the other. But what has changed now is the disappearance of human intervention. Technologies of the 21st century are directly interfacing with humans without the help of keyboards, mice, and monitors. In a significant way, artificial intelligence has simplified the lives of patients, doctors, and hospital administrators by taking over many time-consuming and labor-intense jobs. Apparently, the influence of artificial intelligence to streamline medical facilities has gone out of the hospital doors. Now, many healthcare companies are also embracing technology to come up with innovative solutions that could solve blooming healthcare issues. Even though the present is already fascinating enough to feel awe, the future is anticipated to unfold in a further sophisticated manner. Imagine being able to analyze data on patients visit to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system, such as social media, purchases made using credit cards, census records, internet search activity logs that contain valuable health information. This is the future we’ll be stepping into very soon. Unfortunately, ethical issues are embedded with it. But as technology moves further, the privacy concepts will also evolve with it to create a safer medical experience.
Top 10 Artificial Intelligence Courses In The Year 2023
These top 10 Artificial Intelligence courses in the year 2023 are going to help you
The future of the world is artificial intelligence. Every website and App these days uses AI for the majority of its operations. Nowadays, they have even been used to place your face on other characters in gaming and non-gaming apps. They are utilized for facial recognition locks, registering and verifying your security for transactions, and other purposes. All of these and more are made feasible by artificial intelligence.
In this article, we have explained the top 10 artificial intelligence courses in the year 2023. Read to know more about AI courses in 2023 in detail.
Best Artificial Intelligence CoursesHere we have put together a list of the Best AI courses offered by prestigious colleges and online discussion boards.
1. Artificial Intelligence A-ZTo develop AI designs for practical applications, this course provides a broad understanding of artificial intelligence techniques like machine learning, data science, and deep learning.
The Course Covers Topics Such as:
The Applications of Artificial Intelligence.
Designs for artificial intelligence.
acquiring knowledge of A3C.
command sophisticated AI models.
Create virtual autonomous vehicles.
2. Artificial Intelligence Certification Program provided by Stanford UniversityThis is the best artificial intelligence course, making it perfect for software engineers who will eventually work with AI as well as those studying computer programming and language programming.
The Course Covers Topics Such as:
Both knowledge representation and machine learning.
Models based on logic and probability.
artificial intelligence, robotics, and visual learning.
3. AI Course for Everyone by Coursera
This course is provided by chúng tôi which was founded by Stanford University’s renowned adjunct professor and Google Brain founder Andrew Ng.
The course covers topics such as:
The purposes of machine learning.
Machine learning expertise and non-technical explanations of deep learning.
Brief tests to aid in understanding complex and new ideas
4. Python-Based Introduction to Artificial Intelligence from EdXThe Certified Certificate in Using AI with Python is an exceptional opportunity provided by Harvard University to learn and use AI technology while utilizing challenging mathematical ideas.
The course covers topics such as:
To work with Markov models and Bayesian networks.
Using graph search algorithms.
Designing for constraint fulfilment.
5. Coursera’s IBM Applied AI Certification ProgramIBM provides this fantastic chance for users to learn Python, build chatbots and virtual assistants for their companies, and get knowledge of neural networks, machine learning, and deep learning.
The course addresses subjects like:
APIs, Python, and IBM’s Watson AI service are used to construct AI-powered systems with little to no coding.
Vision approaches are used to upload numerous design and classification models.
6. Watson Application from edXThis course offers a deep understanding and application of IBM’s Watson to develop AI algorithms that are smarter and can perform replies and functions that are more believable and humane.
The course addresses subjects like:
AI may be programmed to analyze and interpret large data sets and apply particular functions to increase productivity.
Constructing Watson with tone and client preferences in mind.
7. 2023 Artificial Intelligence: Create the Most Potent AIThis course teaches students about Augmented Random Search (ARS), a technique used by large corporations to create sophisticated artificial intelligence models.
The course addresses subjects like:
Develop programming for artificial intelligence.
Create robust AI algorithms.
How to use the algorithm in the real world and the theory behind ARS?
8. Udemy’s Master Class in AIThe best course available for learning how to create robust hybrid AI and artificial intelligence models.
The course addresses subjects like:
Figuring out how to use fully linked neural networks.
Working with evolutionary strategies, policy gradients, and genetic algorithms.
NeuroEvolution and deep learning of recurrent neural networks
System development for hybrid intelligence.
9. Udemy’s Introduction to AI for ManagersHave you ever wondered how automatically multi-layered neural networks learn and adapt? Using in-depth machine learning and deep learning techniques, this course provides you with a thorough understanding of the technical components of AI.
The course addresses subjects like:
Teaches how to effectively manage AI projects for outstanding outcomes.
10. Google AI Powered by GoogleThe course addresses subjects like:
Fundamental knowledge of data clustering, recommendation systems, explorables AI, and machine learning.
How to prepare data, test, and debug machine learning?
Artificial Intelligence In 2023: Urgency And Pragmatism
Where is artificial intelligence going in 2023? According to a recent Forrester research report, many companies feel an urgency to reap the benefits of AI. Indeed, artificial intelligence is seen as a propulsive driver of competitive success. If you’re not on the AI train, your competitors are leaving you at the station.
And yet there’s also a growing need to grapple with adopting AI in a pragmatic manner. AI can be wildly expensive, and companies have gotten burned doing “moonshot” projects. The mood is, “okay, we’ve heard that AI is big magic — now prove it to me.”
To shed light on the rapidly AI sector, I spoke with JP Gownder, Vice President, principal analyst, Forrester research. Gownder co-authored a recent report, AI 2023.
This discussion covers:
What is the current state of AI adoption?
Specific predictions for AI’s future.
How companies are purchasing artificial intelligence solutions – from AI companies?
Expectations for AI in 2023
Scroll down to see an edited transcript of highlights from”Data Analytics 2023.”
Download the podcast:
Tech trends come and go, and they have their 15 minutes of fame. Yet it feels like artificial intelligence is more foundational – it’s the Mega trend that will eat all other trends. Agree or is that just hyperbole?
“I think that the future of AI is always bright, and that’s one of the problems we’ve had with AI. In the dawn of the computing, computer science era in the 1940s and ’50s, before we even had proper hardware, some computer scientists thought that we would solve the general AI problem by the 1970s. And of course, that didn’t happen.
“We look forward and then there was what was called an AI winter, which was a period of disillusionment when people realized that practically speaking, certain problems could not be overcome.
“But in the last three or four years, we’ve entered this new phase of AI development where not only have hardware and software become more capable to move to the cloud that you mentioned and other factors making us feel like AI could be done effectively. However, when we come right down to it, we need AI to be in service of something, hopefully it’s in service of improved efficiency or customer obsession or operational effectiveness. And we have fairly mixed record at the moment on that.
“Finally, I want to say, AI is such a plethora of different technologies that it’s all over the place. In some areas, AI will very quickly become table stakes. If you look at what Alibaba has done in the retail sector, or Amazon in terms of personalization and choice and predictive analytics around what people want to buy. Well look, that is becoming really powerful and important, but other areas of AI are quite lagging and are definitely hyperbole today.”
“So our data also shows that about 53% of data and analytics decision makers say that their company is in the implementing phase or they’ve already implemented some AI. But that is to say that within those organizations, that could be a small project.
“So 53% of companies are doing something, but that might be a Tensor Flow model running on one workstation for a data scientist. So it is again, rather variable.
“And I would say that in the grand scheme of things, we remain at an early stage of this, and there’s reasons for that, this is not easy to do well. We don’t necessarily have data hygiene that’s allowed us to tap into the right kind of data, we have these data silos and stuff. So, the foundation of AI being data, that’s a problem.
“We have a lack of governance, most companies don’t know from ethics to explainability to exactly who to participate in the process of overseeing governance. Very few companies have gone deeply down that road.”
How are companies actually purchasing AI solutions?
“So there’s a wide range. Again, with AI being such a broad area, a couple of important factors here. Number one, it can be very challenging even for a large company to hire AI talent. So I was talking to an insurance company that’s global in span. It’s a huge company, based in the Midwest, however. This is not an area where there’s a lot of local talent. They could choose to hire someone who sits in San Francisco, but it might be a bit of an inhibitor. And it’s also the cost of that talent can grow to the millions of dollars.
“Secondarily, you may not even have the basic organizational capability to set something like this up. And so you may choose to go outside. But there could be other cases where you do have a data science team and what you’re doing is more incremental. You’re building using open-source kind of solutions like Tensor Flow, which is common in the ML and deep learning spaces. And maybe you can start internally.
“So what we’re finding is a distribution, but many companies do turn to external experts, companies that are able to offer data hygiene, data engineering services that are offering a variety of different kinds of analytical techniques. Or they’re working off of a big platform like Microsoft Azure or AWS, which have their own AI tools from which you can build applications.
“Finally, there’s also what you could think of as everyday or embedded AI, which is to say, ‘I am already using software and the vendor of that software has decided to add AI features to make my experience better.’
“When you log into the latest version of PowerPoint, you start building a slide, it actually tells you, ‘Here are some things you can do. Do you wanna do these things?’ And that’s actually powered by AI. So there’s a large span of different ways to do this from systems integrators, who are gonna do big projects, to existing software providers to building off of a platform to maybe a little bit of internal work.”
In closing, I’d like to get your thoughts about where we are in the bigger picture – AI going into 2023 – and if there’s any way for companies to get ahead of that wave as you see it happening.
We’re going to continue to see a move toward, ‘Prove it to me. What are the business results measurement?’ I think that some of the moonshot projects in a year in which we don’t necessarily expect recession, but reasonably slow economic growth, companies are turning pragmatic. That was the theme for 2023. We think that will continue into 2023.
“We also see that AI will be applied to, I think, more specific business problems rather than these broader ones. We will also see an element of AI riding in on certain other technologies. Principally or in the leading case, it’ll be robotic process automation.
“So those are some big trends that we see next year. AI will be a critical part of the conversation for enterprise tech, but it will become a pragmatic part of that conversation.”
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