A week after an employee of the world’s largest asset management company, BlackRock, described how the company attempts to stay out of the media spotlight while buying politicians and profiting off of war (according to undercover footage obtained by the O’Keefe Media Group), we thought it worth a look at just what companies does the 34-year-old company have the most control of.

As a reminder, in footage secretly recorded by undercover journalists in New York, a BlackRock recruiter named Serge Varlay explains how the investment company is able to “run the world.”

“They [BlackRock] don’t want to be in the news. They don’t want people to talk about them. They don’t want to be anywhere on the radar,” Varlay said.

Varlay told a OMG journalist in the footage that BlackRock manages $20 trillion worldwide (it’s actually around $9 trillion). “It’s incomprehensible numbers,” he said.

“You can take this big f***ton of money and buy people, I work for a company called BlackRock… It’s not who is the president it’s who is controlling the wallet of the president.

You could buy your candidates. First, there is the senators these guys are f***in cheap. Got 10 grand you can buy a senator. I’ll give you 500k right now. It doesn’t matter who wins, they’re in my pocket.”

– Serge Varlay

Article: These are the companies that blackrock “controls” the most of 

Daniel Leufer, a senior policy analyst at digital rights nonprofit Access Now, says the changes that OpenAI has made in recent weeks are OK but that it is only dealing with “the low-hanging fruit” when it comes to data protection. “They still have done nothing to address the more complex, systemic issue of how people’s data was used to train these models, and I expect that this is not an issue that’s just going to go away, especially with the creation of the EDPB taskforce on ChatGPT,” Leufer says, referring to the European regulators coming together to look at OpenAI.

“Individuals also may have the right to access, correct, restrict, delete, or transfer their personal information that may be included in our training information,” OpenAI’s help center page also says. To do this, it recommends emailing its data protection staff at dsar@openai.com. People who have already requested their data from OpenAI have not been impressed with its responses. And Italy’s data regulator says OpenAI claims it’s “technically impossible” to correct inaccuracies at the moment.

You should be cautious of what you tell ChatGPT, especially given OpenAI’s limited data-deletion options. The conversations you have with ChatGPT can, by default, be used by OpenAI in its future large language models as training data. This means the information could, at least theoretically, be reproduced in answer to people’s future questions. On April 25, the company introduced a new setting to allow anyone to stop this process, no matter where in the world they are.

When logged in to ChatGPT, click on your user profile in the bottom left-hand corner of the screen, click Settings, and then Data Controls. Here you can toggle off Chat History & Training. OpenAI says turning your chat history off means data you input into conversations “won’t be used to train and improve our models.”

As a result, anything you enter into ChatGPT—such as information about yourself, your life, and your work—shouldn’t be resurfaced in future iterations of OpenAI’s large language models. OpenAI says when chat history is turned off, it will retain all conversations for 30 days “to monitor for abuse” and then they will be permanently deleted.

When your data history is turned off, ChatGPT nudges you to turn it back on by placing a button in the sidebar that gives you the option to enable chat history again—a stark contrast to the “off” setting buried in the settings menu

Article: How to delete your data from chatGPT

When it comes to artificial intelligence (AI), the advances we saw in 2021 pale in comparison to those that occurred last year, and AI shows no signs of slowing down. Industries from financial services to healthcare to manufacturing are adopting AI-enabled solutions to restructure how they operate as well as to solve previously intractable problems. Building on my AI predictions from last year, in this article I will explore five ways in which AI is poised to transform our society in the near future.

1. Generative AI will keep ramping.

Generative AI had an explosive year in 2022. Popular systems like DALL-E 2, Stable Diffusion and Midjourney can produce incredibly detailed images from a text prompt in a matter of seconds, irrevocably altering the landscape of graphic design. Tools like ChatGPT allow users to converse with a highly capable chatbot capable of answering questions, composing emails, creating code and even writing entire essays from scratch. The changes from previous iterations of Generative AI have been staggering—DALL-E 2’s picture-perfect generations are leaps and bounds above its predecessor, despite less than a two-year gap between the release of the models.

2. Companies will embrace AI across creative workflows.

Generative AI is also on track to become much more widely adopted for commercial purposes and as a part of creative workflows and practices. Companies could soon routinely leverage generative text and imagery to create entirely new content and marketing displays. Instead of spending hours churning out drafts, you could create hyper-personalized content rapidly, reducing your marketing budget and improving effectiveness in the process.

AI generative music could also contribute to this evolution in marketing, becoming a staple in commercial jingles, background video music and other online media. Furthermore, generative imagery has the potential to help create logos, social media posts and other high-quality visuals. I see these capabilities as revolutionizing the marketing industry by switching the focus to AI-assisted content that can resonate with individual user needs.

3. AI in healthcare will propel drug development and personalized medicine.

The healthcare industry is also primed to undergo profound transformation due to AI. AI is currently being used to do gene-sequencing work, predicting and identifying hidden patterns in genomic data to make it easier for us to research diseases and potential treatments. According to Pfizer, AI is also “being trained to predict drug efficacy and side effects, and to manage the vast amounts of documentation and data that support any pharmaceutical product.”

Machine learning can speed up regulatory filings by anticipating questions and using data-driven insights to generate proactive responses.

AI is also providing actionable insights for patient treatment, allowing for the development of more personalized treatment plans. In this way, nuanced insights from physiological data can help predict how a patient will respond to various treatment options, giving doctors the ability to choose the best course of action at the level of the individual patient.

4. We’ll see a greater emphasis on responsible AI and ethics.

With the growing adoption of AI, there is bound to be a greater focus on responsible AI usage. Currently, image generators can create almost life-like images, which has raised concerns about artistic integrity and abuse. AI has also been accused of propagating human biases, namely because AI technologies are trained using data provided by humans. These are core issues that companies will need to address in order to successfully integrate robust AI systems into production environments.

Some nascent solutions have us mitigating the potential for bias by examining the underlying data used to train AI systems. Businesses will also need to ensure AI transparency among employees and customers, emphasizing how AI is being used in order to alleviate stakeholder concerns. Lastly, businesses will also need to bolster their data privacy, security and usage practices—proactively navigating new IP infringement and data leakage risks.

5. AI will redefine how businesses interact with their customers.

AI is likely to soon transform the ways businesses interact with customers. In fact, AI-powered digital assistants and chatbots are already capable of having human-like conversations using natural language processing (NLP), answering ever more sophisticated questions and personalizing interactions based on customer intent. Soon, companies may be able to offer completely individualized and bespoke experiences for customers. Using individual customer data, AI could be used to predict customer needs and expectations better, reducing handling times and helping to identify future trends.

AI can also be used to help optimize supply chains by analyzing huge volumes of data to predict demand across multiple product segments and geographies. Early adopters have been able to use AI solutions such as processing optimization, predictive maintenance and inventory analytics to manage their wider value chain. While only a handful of businesses are currently using such AI-based solutions, these are likely to permeate across all industries as businesses strive to remain competitive.

Final Thoughts

AI is still expanding and making great strides. It’s poised to change our business landscape—from generative AI’s in marketing to its uses in tailored therapy and medication research. With the growing adoption of AI, I also see the need for a greater emphasis on responsible usage and ethics. Overall, it’s beyond exciting to imagine the potential advancements and improvements that AI can bring in the near future.

Article: Five artificial intelligence predictions for the near future

Artificial intelligence (AI) leaders, consultants and vendors look at enterprise trends and make their predictions.

You may not agree with every one of these — but in honor of 2023, these are 23 top AI and ML predictions experts think will be spot-on for the coming year:

1. AI will be at the core of connected ecosystems

“In 2023, we’re going to see more organizations start to move away from deploying siloed AI and ML applications that replicate human actions for highly specific purposes and begin building more connected ecosystems with AI at their core. This will enable organizations to take data from throughout the enterprise to strengthen machine learning models across applications, effectively creating learning systems that continually improve outcomes. For enterprises to be successful, they need to think about AI as a business multiplier, rather than simply an optimizer.”

—  Vinod Bidarkoppa, CTO of Sam’s Club and SVP of Walmart 

2. Generative AI will transform enterprise applications

“The hype about generative AI becomes reality in 2023. That’s because the foundations for true generative AI are finally in place, with software that can transform large language models and recommender systems into production applications that go beyond images to intelligently answer questions, create content and even spark discoveries. This new creative era will fuel massive advances in personalized customer service, drive new business models and pave the way for breakthroughs in healthcare.”

— Manuvir Das, senior vice president, enterprise computing, Nvidia

3. AI will completely transform security, risk and fraud

“We’re seeing AI and powerful data capabilities redefine the security models and capabilities for companies. Security practitioners and the industry as a whole will have much better tools and much faster information at their disposal, and they should be able to isolate security risks with much greater precision. They’ll also be using more marketing-like techniques to understand anomalous behavior and bad actions. In due time, we may very well see parties using AI to infiltrate systems, attempt to take over software assets through ransomware and take advantage of the cryptocurrency markets.”

– Ashok Srivastava, senior vice president and chief data officer, Intuit

4. Open source ML tools will gain greater market share

“Next year teams that focus on ML operations, management and governance will have to do more with less. Because of this, businesses will adopt more off-the-shelf solutions because they are less expensive to produce, require less research time and can be customized to fit most needs. MLOps teams will also need to consider open-source infrastructure instead of getting locked into long-term contracts with cloud providers. Open source delivers flexible customization, cost savings and efficiency. Especially with teams shrinking across tech, this is becoming a much more viable option.”

— Moses Guttman, CEO, ClearML

5. Deep learning opportunities will boost demand for GPUs

“The biggest source of improvement in AI has been the deployment of deep learning — and especially transformer models — in training systems, which are meant to mimic the action of a brain’s neurons and the tasks of humans. These breakthroughs require tremendous compute power to analyze vast structured and unstructured datasets. Unlike CPUs, graphics processing units (GPUs) can support the parallel processing that deep learning workloads require. That means in 2023, as more applications founded on deep learning technology emerge to do everything from translating menus to curing disease, demand for GPUs will continue to soar.”

— Nick Elprin, CEO, Domino Data Lab

6. AI will create meaningful coaching experiences

“Modern AI technology is already being used to help managers, coaches and executives with real-time feedback to better interpret inflection, emotion and more, and provide recommendations on how to improve future interactions. The ability to interpret meaningful resonance as it happens is a level of coaching no human being can provide.”

— Zayd Enam, CEO, Cresta

7. Geopolitical shifts will slow AI adoption

“As fear and protectionism create barriers to data movement and processing locations, AI adoption will slow down. Macroeconomic instability, including rising energy costs and a looming recession, will hobble the advancement of AI initiatives as companies struggle just to keep the lights on.”

— Rich Potter, CEO, Peak

8. The role of AI and ML engineers will become mainstream

“Since model deployment, scaling AI across the enterprise, reducing time to insight and reducing time to value will become the key success criteria, AI/ML engineers will become critical in meeting these criteria. Today a lot of AI projects fail because they are not built to scale or [to] integrate with business workflows.”

— Nicolas Sekkaki, GM of applications, Data and AI, Kyndryl

9. Multi, hybrid-cloud MLOps and interoperability will be key

“As the AI/ML market continues to flood with new solutions, as evident by the volume of startups and VC capital deployed in the space, enterprises have found themselves with a collection of niche, disparate tools at their disposal. In 2023, enterprises will be more conscious of selecting solutions that will be more interoperable with the rest of their ecosystem, including their on-premises footprint and across cloud providers (AWS, Azure, GCP). Additionally, enterprises will gravitate towards a handful of leading solutions as the disparate tools mature and come together in bundles as standalone solutions.”

— Anay Nawathe, principal consultant, ISG

10. Advanced ML will enable no-code AI 

“Advanced machine learning technologies will enable no-code developers to innovate and create applications never seen before. This evolution may pave the way for a new breed of development tools. In a likely scenario, application developers will ‘program the application’ by describing their intent, rather than describing the data and the logic as they’d do with low-code tools of today.”

— Esko Hannula, SVP of product management, Copado

11. With spending down, AI will shift to practical applications

“This past year was filled with incredibly impressive technological advancements, popularized by ChatGPT, DALL-E 2, Galactica and Facebook’s Make-A-Video. These massive models were made possible largely due to the availability of endless volumes of training data, and huge compute and infrastructure resources. Heading into 2023, funding for true blue-sky research will slow down as organizations become more conservative in spending to brace for the looming recession and will shift from investing in fundamental research to more practical applications. With more companies becoming increasingly frugal to mitigate this imminent threat, we can anticipate increased use of pre-trained models and more focus on applying the advancements from previous years to more concrete applications.”

—John Kane, head of signal processing and machine learning, Cogito

12. ChatGPT will change the contact center, but not the way you think

“Chatbots are the obvious application for ChatGPT, but they are probably not going to be the first ones. First, ChatGPT today can answer questions, but it cannot take actions. When a user contacts a brand, they sometimes just want answers, but often they want something done — process a return, or cancel an account, or transfer funds. Secondly, when used to answer questions, ChatGPT can answer based on knowledge [found] on the internet. But it doesn’t have access to knowledge which is not online. Finally, ChatGPT excels at generation of text, creating new content derived from existing online information. When a user contacts a brand, they don’t want creative output — they want immediate actions. All of these issues will get addressed, but it does mean that the first use case is probably not chatbots.”

— Jonathan Rosenberg, CTO, Five9

13. AI will drive the future of customer experience

“Digital engagement has become the default rather than the fallback, and every interaction counts. While the emergence of automation initially resolved basic FAQs, it’s now providing more advanced capabilities: personalizing interactions based on customer intent, empowering people to take action and self-serve, and making predictions on their next best action.

“The only way for businesses to scale a VIP digital experience for everyone is with an AI-driven automation solution. This will become a C-level priority for brands in 2023, as they determine how to evolve from a primarily live agent-based interaction model to one that can be primarily serviced through automated interactions. AI will be necessary to scale operations and properly understand and respond to what customers are saying, so brands can learn what their customers want and plan accordingly.”

— Jessica Popp, CTO of Ada

14. AI model marketplaces will emerge

“Coming soon are industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models in their business without having to create and manage the model lifecycle. Businesses will simply subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.”

— Bryan Harris, executive vice president and chief technology officer, SAS 

15. Explainability will create more trustworthy AI

“As individuals continue to worry about how businesses and employers will use AI and machine learning technology, it will become more important than ever for companies to provide transparency into how their AI is applied to worker and finance data. Explainable AI will increasingly help to advance enterprise AI adoption by establishing greater trust. More providers will start to disclose how their machine learning models lead to their outputs (e.g. recommendations) and predictions, and we’ll see this expand even further to the individual user level with explainability built right into the application being used.”

— Jim Stratton, CTO, Workday

16. 2023 will be a major year for federated learning

“Federated learning is a machine learning technique that can be used to train machine learning models at the location of data sources, by only communicating the trained models from individual data sources to reach a consensus for a global model. Therefore instead of using the traditional approach of collecting data from multiple sources to a centralized location for model training, this technique learns a collaborative model. Federated learning addresses some of the major issues that prevail in the current machine learning technique, such as data privacy, data security, data access rights and access to data from heterogeneous sources.”

— David Murray, chief business officer, Devron

17. NLP plus object recognition will take search to the next level

“While most people write scrapers today to get data off of websites, natural language processing (NLP) progress has been made where soon you can describe in natural language what you want to extract from a given web page and the machine pulls it for you. For example, you could say, “Search this travel site for all the flights from San Francisco to Boston and put all of them in a spreadsheet, along with price, airline, time and day of travel.” It’s a hard problem, but we could actually solve it in the next year.”

— Varun Ganapathi, CTO and co-founder, AKASA

18. Advances are coming in real-time speech translation

“With remote work, boundaries are becoming increasingly blurred. Today it’s common for people to work and converse with colleagues across borders, even if they don’t share a common language. Manual translation can become a hindrance that slows down productivity and innovation. We now have the technology to use communication tools such as Zoom that allows someone in Turkey, for example, to speak their native language but allows someone in the U.S. to hear what they’re saying in English. This real-time speech translation ultimately helps with efficiency and productivity while also giving businesses more of an opportunity to operate globally.”

— Manoj Chaudhary, CTO and SVP of engineering, Jitterbit

19. AI-enabled phishing will grow

“By now, everyone has seen AI-created deepfake videos. They are leveraged for a variety of purposes, ranging from reanimating a lost loved one, disseminating political propaganda or enhancing a marketing campaign. However, imagine receiving a phishing email with a deepfake video of your CEO instructing you to go to a malicious URL. Or an attacker constructing more believable, legitimate-seeming phishing emails by using AI to better mimic corporate communications. Modern AI capabilities could completely blur the lines between legitimate and malicious emails, websites, company communications and videos. Cybercrime AI-as-a-Service could be the next monetized tactic.”

— Heather Gantt-Evans, CISO, SailPoint

20. Companies will turn to a hybrid approach to NLP

“In the year ahead, we will see enterprises turn to a hybrid approach to natural language processing combining symbolic AI with ML, which has shown to produce explainable, scalable and more accurate results while leaving a smaller carbon footprint. Companies will expand automation to more complex processes, requiring accurate understanding of documents, and extending their data analytics activities to include data embedded in text and documents. Therefore, investments in AI-based natural language technologies will grow. These solutions will have to be accurate, efficient, environmentally sustainable, explainable and not subject to bias. This requires enterprises to abandon the single-technique approach such as just machine learning (ML) or deep learning (DL) for their intrinsic limitations.”

— Luca Scagliarini, chief product officer, Expert.ai

21. AI-generated music will see advancements

“Advancements in AI-generated music will be a particularly interesting development. Now [that] tools exist that generate visual art from text prompts, these same tools will be improved to do the same for music. There are already models available that use text prompts to generate music and realistic human voices. Once these models start performing well enough that the public takes notice, progress in the field of generative audio will accelerate even further. It’s not unreasonable to think, within the next few years, that AI-generated music videos could become reality, with AI-generated video, music and vocals.”

— Ulrik Stig Hansen, president, Encord

22. AI investments will move to fully-productized applications

“There will be less investment within Fortune 500 organizations allocated to internal ML and data science teams to build solutions from the ground up. It will be replaced with investments in fully productized applications or platform interfaces to deliver the desired data analytic and customer experience outcomes in focus. [That’s because] in the next five years, nearly every application will be powered by LLM-based neural network-powered data pipelines to help classify, enrich, interpret and serve.

“[But] productization of neural network technology is one of the hardest tasks in the computer science field right now. It is an incredibly fast-moving space that without dedicated focus and exposure to many different types of data and use cases, it will be hard for internal-solution ML teams to excel at leveraging these technologies.”

— Amr Awadallah, CEO, Vectara

23. AI will empower more efficient devops

“When it comes to devops, experts are confident that AI is not going to replace jobs; rather, it will empower developers and testers to work more efficiently. AI integration is augmenting people and empowering exploratory testers to find more bugs and issues upfront, streamlining the process from development to deployment. In 2023, we’ll see already-lean teams working more efficiently and with less risk as AI continues to be implemented throughout the development cycle.

“Specifically, AI-augmentation will help inform decision-making processes for devops teams by finding patterns and pointing out outliers, allowing applications to continuously ‘self-heal’ and freeing up time for teams to focus their brain power on the tasks that developers actually want to do and that are more strategically important to the organization.”

– Kevin Thompson, CEO, Tricentis

Article: 23 AI predictions for the enterprise in 2023

For the first time, I taught an AI for Cyber Security course at the University of Oxford.

I referred to this paper from Johns Hopkins which covered Deep Neural networks for Cyber Security (A Survey of Deep Learning Methods for Cyber Security) – references below where you can download the full paper for free.

The paper covers various deep learning algorithms in Cyber Security

I summarise from the paper below, the problems in Cyber Security and the deep neural networks algorithms that can address them

Cyber Security problems

Detecting and Classifying Malware: The number and variety of malware attacks are continually increasing, making it more difficult to defend against them using standard methods. DL provides an opportunity to build generalizable models to detect and classify malware autonomously. There are a number of ways to detect malware.

Autonomously classifying malware can provide important information about the source and motives of an adversary without requiring analysts to devote significant amounts of time to malware analysis. This is especially important with the number of new malware binaries and malware families growing rapidly. Classification means assigning a class of malware to a given sample, whereas detection only involves detecting malware, without indicating which class of malware it is.

Domain Generation Algorithms and Botnet Detection (DGA): DGAs are commonly used malware tools that generate large numbers of domain names that can be used for difficult-to-track communications with C2 servers. The large number of varying domain names makes it difficult to block malicious domains using standard techniques such as blacklisting or sink-holing. DGAs are often used in a variety of cyber-attacks, including spam campaigns, theft of personal data, and implementation of distributed denial-of-service (DDoS) attacks.

Drive-By Download Attacks: Attackers often exploit browser vulnerabilities. By exploiting flaws in plugins, an attacker can redirect users away from commonly used websites, to websites where exploit code forces users to download and execute malware. These types of attacks are called drive-by download attacks.

Network Intrusion Detection: Network intrusion detection systems are essential for ensuring the security of a network from various types of security breaches. A number of machine learning and deep learning algorithms are used in network detection.

File Type Identification: Generally, humans are not very effective at identifying data that is being exfiltrated once it has been encrypted. Signature-based approaches are similarly unsuccessful at this task. Therefore, a number of ML/DL techniques can be applied to detect file types

Network Traffic Identification: A set of techniques used to detect network level protocol types.

SPAM Identification: ML and DL algorithms used to detect SPAM

Insider Threat Detection: One of the major cyber security challenges today is insider threat, which results in the theft of information or the sabotaging of systems. The motivations and behaviors of insider threats vary widely; however, the damage that insiders can inflict is significant. A number of ML and DL algorithms are used in the detection of insider threats.

Border Gateway Protocol Anomaly Detection: The Border Gateway Protocol (BGP) is an internet protocol that allows for the exchange of routing and reachability information among autonomous systems. This capability is essential to the functioning of the internet, and exploitation of BGP flaws can result in DDoS attacks, sniffing, rerouting, theft of network topology data, etc. It is therefore essential to identify anomalous BGP events in real time to mitigate any potential damages.

Verification If Keystrokes Were Typed by a Human: Keystroke dynamics is a biometric technique that collects the timing information of each keystroke – this information can be used to identify people or anomalous patterns.

User Authentication: The ability to detect users based on various signals – behavioral and physiological features based on their  activity patterns

False Data Injection Attack Detection: Cyber-physical systems play an important role in critical infrastructure systems, because of their relationship to the smart grid. Smart grids leverage cyber-physical systems to provide services with high reliability and efficiency, with a focus on consumer needs. These smart grids are capable of adapting to power demands in real time, allowing for an increase in functionality. However, these devices rely on information technology, and that technology is susceptible to cyber-attack. One such attack is false data injection (FDI), whereby false information is injected into the network to reduce its functionality or even break it entirely.

 Deep learning detection techniques

The following techniques are used to address Cyber Security problems as per the paper

Full article: Deep Learning techniques for Cyber Security

Unprecedented Urban Growth Needs Exponential Innovation

Globally, as of 2020, 55% of people live in cities according to the World Bank and by 2050 that number is estimated to reach 70% of the worldwide population, totaling nearly 7 billion urban inhabitants. With this unprecedented growth, the challenges of making these environments safe, healthy, productive, and thriving have never been more difficult.

Governments worldwide are also under pressure to reduce costs while at the same time improving and modernizing services. These dual pressures are pushing cities to be smarter and look to innovative tech solutions to help drive outcomes. It is in this context that we see the rise of Artificial Intelligence (AI) and Digital Twins as they are applied more frequently to address the most pressing concerns for city officials and constituents.

What is Artificial Intelligence?

Artificial Intelligence (AI) Applications are defined as enterprise applications powered by intelligent technologies such as machine learning including deep learning for natural language processing, voice/speech recognition, image/video analysis and time series analysis that drive insights, provide predictions, support conversations, recommendations and/or automate tasks and processes. Some areas where AI is being applied in smart cities are the following:

Top 10 Artificial Intelligence Uses Cases having the Greatest Impact on Mission Outcomes in Government

1. Public health promotion, disease prevention and management (including contact tracing)
2. Shared digital workspace, productivity and collaboration tools
3. Employee self-service tools
4. IT infrastructure security monitoring
5. Loans and grants management
6. Environmental monitoring (air quality, water quality, weather monitoring)
7. Digital content, document and workflow management
8. 311 and constituent self-service
9. Facilities and building management
10. Public safety, security, surveillance

What is a Digital Twin?

Digital twins are the extended application of simulation and visualization throughout a digitally transformed organization, for better communication and collaboration using complex, rich data. IDC predicts that by 2023, 25% of successful Smart Cities digital twin platforms will be used to automate processes for increasingly complex, interconnected ecosystems of assets and products.

The digital twin is a solution-based approach capable of producing high-fidelity copies of a city or other geographic space. This digital copy is based on a wealth of potential data sources such as GIS base layers, IoT sensor data, historical data sets, and other “Smart City” sources such as crime data and multichannel nonemergency citizen reporting systems like “311” systems, environmental monitoring, planning and permitting, and transportation infrastructure. Bringing together this breadth of information in a three-dimensional (3D), high-fidelity, single source of information enables organizations to conduct better analysis and more accurate scenario planning.

Walking Before You Run

So you want to get started in using these technologies, well, some fundamentals are in order first. Both AI and Digital Twins rely on high quality data to function well and provide real value to the organization. Taking a hard look at your data ecosystem and building some important foundations can pay off later when more advanced solutions are implemented. Without these questions answered it is a sure path to failed projects and cost overruns.

• How secure is your data?
• In a regulated environment, what can you legally use the data for?
• What does your Master Data Management ecosystem look like?
• Do you have good metadata and data dictionaries available for developers and end users?
• Do you have a secure and standardized way to move data between systems?
• Where do you keep your source code and are you using version control?
• What is your appetite for risk, and do you have a mitigation strategy in place should things go wrong?

Where to Next?

Artificial Intelligence and Digital Twins are only just beginning to become a critical component of how cities operate. This will continue to accelerate as the tools not only become more mature but also become incorporated into the tools and processes that we are already familiar with. Like many paradigm shifts and great leaps in innovation they happen slowly and then out of nowhere are everywhere at once. Getting ahead of the curve and preparing City Hall to take advantage of this change will represent a rare generational opportunity.

Article: The next frontier: AI and Digital twins in Smart cities

Ryan Welsh, founder and CEO of Kyndi, recently compiled the following list of artificial intelligence (AI) and data-related topics that he believes will be important in 2023.

1. The world reaches the era of “peak data scientist.”

The shortfall of data scientists and machine learning engineers has always been a bottleneck in companies realizing value from AI. Two things have happened as result: 1) More people have pursued data science degrees and accreditation, increasing the number of data scientists, and 2) vendors have come up with novel ways to minimize the involvement of data scientists in the AI production rollout.

The coincident interference of these two waves yields “peak data scientist,” because with the advent of foundational models, companies can build their own applications on top of these models rather than requiring every company to train their own models from scratch. Less bespoke model training requires fewer data scientists and MLEs (machine learning engineers) at the same time that more are graduating. In 2023, expect the market to react accordingly, resulting in data science oversaturation.

2. The AI industry will offer more tools that can be operated directly by business users.

Companies have been hiring more and more data scientists and MLEs, but net AI adoption in production has not increased at the same rate. While a lot of research and trials are being executed, companies are not benefiting from production AI solutions that can be scaled and managed easily as the business climate evolves.

In the coming year, AI will start to become more democratized such that less technical people can directly leverage tools that abstract all of the machine learning complexity. Knowledge workers and citizen “data scientists” without formal training in advanced statistics and/or mathematics will be extracting high-value insights from data using these self-service tools, allowing them to perform advanced analytics and solve specific business problems at the speed of the business.

3. Chatbots will chat less and answer questions more.

Humans don’t want to spend more time interacting with machines as if they were talking to people; they really just want their questions answered quickly and efficiently from the start without lengthy wait times or having to choose from myriad options. Although many chatbots accurately execute the specific tasks they were designed to do, they fall far short of end-user expectations because they rarely answer their actual questions.

In 2023, organizations will finally be able to complement chatbots with Natural Language Search capabilities. Because Natural Language Search understands human language and can process unstructured text-based data (documents, etc.), individuals can phrase questions using their own words—as if they were speaking to a person—and receive all of the relevant answers back instantly.

4. Line of business leaders will take matters into their own hands.

Twenty years ago, companies had two choices in the CRM (customer relationship management) space: They could pay millions for a Siebel Systems CRM, or they could pay a fraction of that amount monthly on a per user basis … which ushered in the cloud era. The same thing is happening now for business users when it comes to AI.

In 2023, if the use case provides exceptional value, business users will decide whether it makes sense to hire expensive and difficult-to-recruit data scientists and MLEs, label thousands of datapoints, train and re-train models over months, and repeat this process as the underlying data changes. Alternatively, if the value of this AI project does not justify the significant upfront and ongoing cost, then the organization will find a vendor that can remove all of the complexity for business users.

5. Businesses will finally benefit from their unstructured data.

IDC estimates that by 2025, 80% of all data will be unstructured, or free-form, making it difficult to assess and derive insights. Organizations struggle to extract relevant insights when they search for answers in text data, mainly because the search tools they are using are not capable of effectively and efficiently processing unstructured data.

Recognizing the immense value that is being left on the table, organizations in 2023 will apply practical methods to dramatically improve efficiency and unlock the value that has been elusive for so long. Remote and hybrid work has exacerbated the pain of unsatisfying search outcomes because so many employees work from their own locations and access information at different hours, making information sharing within an organization a major challenge. You can’t simply reach out to your colleague sitting next to you for answers whenever you think necessary. In the coming year, expect to see employees turning to Natural Language Search tools to find relevant information across all structured and unstructured sources.

Article: Five artificial intelligence and data predictions for 2023

01. The axiomatics of yoga. Theory and philosophy of yoga. Fundamental principles of yoga. Types of knowledge. Instincts. Stupidity. Logic. Super logic. Higher intuition.
02. Vedas and yoga. The Vedas ancient mystery source of yoga. Classical, orthodox yoga based on the Vedas.
03. Hatha yoga. The yoga of static body postures or asanas as the necessary basis for physical mental, intellectual and spiritual self-improvement.
04. Mantra yoga. Yoga of vibrations of speech, thought, prana.
05. Kriya yoga. Yoga of dynamic exercises.
06. Yoga of extension. Cyclic yoga exercises.
07. Yoga Shavasana. Higher yoga of nonexistence. Yoga of relaxation, rest, peace of mind and body.
08. Pranayama yoga. Yoga of the breath.
09. Dhyana yoga. Meditation. Meditation in yoga.
10. Karma yoga. Yoga of work without attachment.
11. Bhakti yoga. Yoga of devotion, emotions and feelings.
12. Raja yoga. The yoga of the rulers.
13. Jnana yoga. The yoga of knowledge.
14. Yatra yoga and the yoga of walking on the paths.
15. Yoga of Moune. The yoga of silence. Meditation of silence.
16. Yoga of Tapas. The yoga of asceticism and self-control.
17. Yoga and purification practices. Shatkarmas.
18. Yoga of nutrition and starvation.
19. Yoga Mudras and Bandhas. Special yoga exercises.
20. Vairagya yoga. Yoga of unconsciousness.
21. Yantra yoga. Yoga of the visual perception of forms.
22. Nada yoga. Yoga of music.
23. Yoga of dreaming. Yoga of dreams.
24. Nidra yoga. The yoga of transitional states of being.
25. Rita yoga. The yoga of the dance of the universe and man.
26. Natya yoga. Yoga of the theater and actors.
27. Nyasa yoga. Yoga of touch.
28. Paired yoga. Yoga with a partner.
29. Yoga of visualization. Yoga of imagination.
30. Kundalini yoga. Yoga of vital energy.
31. Laia yoga. The yoga of dissolving delusions.
32. Chakra yoga. Yoga of body structures.
33. The yoga of Rod. Yoga of remembering the ancestors.
34. The yoga of dying. The yoga of overcoming death.
35. Tantra yoga. Yoga of sexual attraction.
36. Yoga of sexual union. Yoga of sex.
37. The yoga of falling in love. The yoga of giving to the universe.
38. Yoga of man and woman. Tantra triad yoga.
39. Dharma yoga. The yoga of finding the way of life.
40. Artha yoga. The yoga of prosperity and business.
41. Kama yoga. The yoga of the lawful pleasures of life.
42. Moksha yoga. The yoga of liberation.
43. Rasa yoga. Yoga of feelings and emotions.
44. Swara yoga. Yoga of controlling the processes of the world.
45. Prajoga. The yoga of superpowers
46. Yoga Sutra Patanjali. Classical yoga.
47. Anandalahari yoga. Sri Vidya mother of tantras.
48. Advaita Vedanta yoga. Yoga of overcoming maya.
49. Shiva Samhita yoga. Yoga of the devotees of Shiva.
50. Bhagavat gita yoga. Yoga war of duty.
51. Tibetan yoga Naropa. Tantra yoga of buddhism.
52. Shivachandra yoga. Philosophy principles of tantra.
53. Tantra yoga. Shakta and shakti.
54. Tantra yoga. Hymns to the goddess.
55.Tantra mantra yoga garland of letters.
56. Yoga learning from the four parts. Yoga Vaikhari uchcharana-reading aloud. Yoga Lekhana-writing. Yoga Diksha-transmission of knowledge. Yoga Smarana-memories.
57. Yoga Swadhyaya. Yoga of continuous learning and self-education in yoga Patanjali.

Source: https://openyoga.ru/vidyrasdely/

Article: 75 Insightful Facts About Artificial Intelligence

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