An Early Test of The Adams Administration’s Values and Tech Prowess – Gotham Gazette

Participatory government in-person (photo: New York City Council)

In 2019, New Yorkers voted overwhelmingly in favor of establishing a Civic Engagement Commission that would modernize how the city and its residents worked together to identify and solve problems at the most local levels. That commission hasnt been earning itself many headlines, but the participation platform it set up at participate.nyc.gov could support world-class civic engagement programs. Whether or not the administration of Mayor Eric Adams uses it to do that will be an early test of its basic competency and technology prowess.

Participate.nyc.gov is much more than a basic government website. Its a deployment of an open source participatory democracy platform called Decidim. First founded in Barcelona in 2016, Decidim is free and open source software used by dozens of municipal governments around the world including Helsinki, Mexico City, Zurich, and Milan. The software is a successor of Consul, a similar platform that facilitated genuinely innovative and wildly successful crowd-sourced city planning, participatory budgeting, and decision-making in Madrid, Spain.

The idea behind Decidim and Consul is simple: give the public a single, unified, open source platform and standard set of tools for participating in local civic engagement programs. These platforms allow city residents to be organized into different types of districts to have discussions, make proposals, vote on projects, take surveys, and generate the type of feedback that city agencies and elected officials can and should use to understand how best to improve our neighborhoods, city, and government operations.

Since New York Citys deployment of Decidim isjust beginning to be used in a few City Council districts participatory budgeting processes, its difficult to see the platforms potential. To do that, its best to visit decidim.barcelona (turn on Google Translate if you dont read Spanish) and see how theyre using the software. On that site youll see two main menu items, which translate to Participatory Processes and Participation Bodies, which is more easily understood as Processes and Spaces.

Processes are civic engagement programs, such as a participatory budgeting cycle, a city planning project review, or a charter revision.

Spaces are groups of people, often divided by their district, operating under a defined set of rules about membership and governance.

By applying processes to spaces, the Decidim system deployed at participate.gov.nyc could host many of the citys existing civic engagement processes immediately, right out of the box, with no need for any expensive custom development.

Here are some examples:

-City Council members and districts could use it for participatory budgeting;

-Community boards could publish news, events, meeting minutes, files, videos, surveys and more replacing their websites;

-City commissions could replace their websites with Decidim as well, and use its collaborative editing and commenting features to enable residents to attach their ideas and comments to specific language in a document.

Beyond these basics, the system could be used for so much more: to

-host discussions about pending legislation;

-gather feedback about land use issues;

-provide a unified calendar of city agency outreach events;

-facilitate petitioning the city council to introduce legislation.

The list could go on.

All of these new tools and features can frighten politicians and civil servants because theyre experts in the current systems for public engagement and any change could alter the power dynamics to which theyve become accustomed and dominate. As such, there is natural resistance to utilization of different, better platforms and processes. Fortunately, Mayor Adams claims to be tech literate, eager to reform city government, and focused on public participation.

During the mayoral election, Adams pledged to establish MyCity, a single portal for all City services and benefits. One of the key services and benefits the city offers its residents is civic engagement. As such, a unified platform that many different agencies use for civic engagement purposes fits nicely with Adams' vision.

And, as an open source platform built with the popular framework Ruby on Rails, Decidim is a platform that the city can own and run itself, without needing to pay expensive IT consultants or exorbitant licensing fees. Indeed, it's the perfect project for the Digital Service Organization (DSO) the city should have already launched.

Indeed, the participate.nyc.gov system could become an integral component to the MyCity dream. Decidims login system uses open standards that could and should be integrated with the inevitable user authentication system that is a prerequisite for MyCity. And its data, formatted into configurable open data feeds, can and should flow elegantly into other city information management systems, such as City Record event feeds or City Planning project pages.

To see the true value of Decidim, the Adams administration must develop an understanding of the open source concept that makes it possible. Its hard for many people to understand how sophisticated software like Decidim could be available on the internet to download for free, with no limitations on how or by whom it's used. It seems too good to be true, but it is.

Open source technologies like Linux, WordPress, and Bitcoin get a lot of the headlines, but there are actually hundreds of thousands of open source applications out in the world, and that number is expanding all the time. Most of those applications are components that must be combined with other ones to make a system, but some are full-fledged applications like Decidim.

As Ive mentioned in other articles, open source is transforming how the government delivers services all over the world, but New York Citys IT bureaucracy and poor leadership have not adopted proven techniques to benefit from these advancements because powerful special interests make tremendous amounts of money by keeping the city in the technological dark ages.

Companies that run core city software systems like Microsoft, Dell, Tyler, ESRI, Accenture, and others want to keep the city addicted to their proprietary software systems. To achieve that goal, these companies have fused themselves with city agencies like the Department of Information Telecommunications and Technology (DoITT), which are much more comfortable signing expensive software contracts with these companies than they are deploying and managing open source software systems themselves. Meanwhile, city technology executives routinely work at vendors before and after their time in government. The revolving door spins very quickly.

If Mayor Adams develops an understanding of how to effectively use open source software to make the city more efficient, effective, and equal, then there is no limit to the amount he could achieve. He has a great opportunity to start off on the right foot by organizing a DSO unit to manage the open source Decidim software at participate.nyc.gov and aggressively utilize that software to deliver New Yorkers the world-class civic engagement experiences we deserve.

Delivering compelling civic engagement programming through participate.nyc.gov is a great way for Adams to prove he has the technological prowess and genuine desire for reform that he claimed to have during the campaign.

Its time to deliver.

***Devin Balkindis a nonprofit executive, civic technologist, and startup advisor running for Public Advocate as the Libertarian Party nominee. On Twitter@DevinBalkind.

Originally posted here:
An Early Test of The Adams Administration's Values and Tech Prowess - Gotham Gazette

12 examples of artificial intelligence in everyday life – ITProPortal

In the article below, you can check out twelve examples of AI being present in our everyday lives.

Artificial intelligence (AI) is growing in popularity, and it's not hard to see why. AI has the potential to be applied in many different ways, from cooking to healthcare.

Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. In fact - it's already here.

They work and continue to advance by using lots of sensor data, learning how to handle traffic and making real-time decisions.

Also known as autonomous vehicles, these cars use AI tech and machine learning to move around without the passenger having to take control at any time.

Let's begin with something really ubiquitous - smart digital assistants. Here we are talking about Siri, Google Assistant, Alexa and Cortana.

We included them in our list because they can essentially listen and then respond to your commands, turning them into actions.

So, you hit up Siri, you give her a command, like "call a friend," she analyzes what you said, sifts through all the background noise surrounding your speech, interprets your command, and actually does it, all in a couple of seconds.

The best part here is that these assistants are getting smarter and smarter, improving every stage of the command process we mentioned above. You don't have to be as specific with your commands as you were just a couple of years ago.

Furthermore, virtual assistants have become better and better at figuring out filtering useless background noise from your actual commands.

One of the most well-known AI initiatives is a project run by Microsoft. It comes as no surprise that Microsoft is one of the top AI companies around (though it's definitely not the only one).

The Microsoft Project InnerEye is state-of-the-art research that can potentially change the world.

This project aims to study the brain, specifically the brain's neurological system, to better understand how it functions. The aim of this project is to eventually be able to use artificial intelligence to diagnose and treat various neurological diseases.

The college students' (or is it professor's?) nightmare. Whether you are a content manager or a teacher grading essays, you have the same problem - the internet makes plagiarism easier.

There is a nigh unlimited amount of information and data out there, and less-than-scrupulous students and employees will readily take advantage of that.

Indeed, no human could compare and contrast somebody's essay with all the data out there. AIs are a whole different beast.

They can sift through an insane amount of information, compare it with the relevant text, and see if there is a match or not.

Furthermore, thanks to advancement and growth in this area, some tools can actually check sources in foreign languages, as well as images and audio.

You might have noticed that media recommendations on certain platforms are getting better and better, Netflix, YouTube, and Spotify being just three examples. You can thank AIs and machine learning for that.

The three platforms we mentioned take into account what you have already seen and liked. That's the easy part. Then, they compare and contrast it with thousands, if not tens of thousands, of pieces of media. They essentially learn from the data you provide, and then use their own database to provide you with content that best suits your needs.

Let's simplify this process for YouTube, just as an example.

The platform uses data such as tags, demographic data like your age or gender, as well as the same data of people consuming other pieces of media. Then, it mixes and matches, giving you your suggestions.

Today, many larger banks give you the option of depositing checks through your smartphone. Instead of actually walking to a bank, you can do it with just a couple of taps.

Besides the obvious safeguards when it comes to accessing your bank account through your phone, a check also requires your signature.

Now banks use AIs and machine learning software to read your handwriting, compare it with the signature you gave to the bank before, and safely use it to approve a check.

In general, machine learning and AI tech speeds up most operations done by software in a bank. This all leads to the more efficient execution of tasks, decreasing wait times and cost.

And while we are on the subject of banking, let's talk about fraud for a little bit. A bank processes a huge amount of transactions every day. Tracking all of that, analyzing, it's impossible for a regular human being.

Furthermore, how fraudulent transactions look changes from day to day. With AI and machine learning algorithms, you can have thousands of transactions analyzed in a second. Furthermore, you can also have them learn, figure out what problematic transactions can look like, and prepare themselves for future issues.

Next, whenever you apply for a loan or maybe get a credit card, a bank needs to check your application.

Taking into account multiple factors, like your credit score, your financial history, all of that can now be handled by software. This leads to shorter approval wait times and a lower margin for error.

Many businesses are using AI, specifically chatbots, as a way for their customers to interact with them.

Chatbots are often used as a customer service option for companies that do not have enough staff available at any given time to answer questions or respond to inquiries.

By using chatbots, these companies can free up staff time for other tasks while still getting important information from their customers.

These are a godsend during heavy traffic times, like Black Friday or Cyber Monday. They can save your company from getting overwhelmed with questions, allowing you to serve your customers much better.

Now, this is something we can all be thankful for - spam filters.

A typical spam filter has a number of rules and algorithms that minimize the amount of spam that can reach you. This not only saves you from annoying ads and Nigerian princes, but it also helps against credit card fraud, identity theft, and malware.

Now, what makes a good spam filter effective is the AI running it. The AI behind the filter uses email metadata; it keeps an eye on specific words or phrases, it focuses on some signals, all for the purpose of filtering out spam.

This everyday AI aspect got really popular through Netflix.

Namely - you might have noticed that a lot of thumbnails on websites and certain streaming apps have been replaced by short videos. One of the main reasons this got so popular is AI and machine learning.

Instead of having editors spend hundreds of hours on shortening, filtering, and cutting up longer videos into three-second videos, the AI does it for you. It analyzes hundreds of hours of content and then successfully summarizes it into a short bit of media.

AI also has potential in more unexpected areas, such as cooking.

A company called Rasa has developed an AI system that analyzes food and then recommends recipes based on what you have in your fridge and pantry. This type of AI is a great way for people who enjoy cooking but don't want to spend too much time planning out meals ahead of time.

If there is one thing we can say about AI and machine learning, it is that they make every tech they come in contact with more effective and powerful. Facial recognition is no different.

There are now many apps that use AI for their facial recognition needs. For example, Snapchat uses AI tech to apply face filters by actually recognizing the visual information presented as a human face.

Facebook can now identify faces in specific photos and invite people to tag themselves or their friends.

And, of course, think about unlocking your phone with your face. Well, it needs AI and machine learning to function.

Let's take Apple Face ID as an example. When you are setting it up, it scans your face and puts roughly thirty thousand dos on it. It uses these dots as markers to help it recognize your face from many different angles.

This allows you to unlock your phone with your face in many different situations and lighting environments while at the same time preventing somebody else from doing the same.

The future is now. AI technology will only continue to develop, to grow and to become more and more vital for every industry and almost every aspect of our everyday lives. If the above examples are to be believed, it's only a matter of time.

Artificial intelligence will continue developing and being present in new areas of our lives in the future. As more innovative applications come out, we'll see more ways that AI can make our lives easier and more productive!

Read more from the original source:
12 examples of artificial intelligence in everyday life - ITProPortal

Johns Hopkins University teams up with Amazon to explore the power of artificial intelligence – – Baltimore Fishbowl

Johns Hopkins University and Amazon are partnering on a new initiative to make advancements in artificial intelligence (AI).

The JHU + Amazon Initiative for Interactive AI (AI2AI) will focus on machine learning, computer vision, natural language understanding, and speech processing.

The five-year, Amazon-funded initiative will support fellowships, collaborative research projects led by Hopkins faculty, and research events and activities to accelerate AI research in the Baltimore-Washington, D.C. region.

Hopkins is already renowned for its pioneering work in these areas of AI, and working with Amazon researchers will accelerate the timetable for the next big strides, said Sanjeev Khudanpur, an associate professor at Hopkins Whiting School of Engineering, in a statement.

AI has tremendous potential to enhance human abilities, and to reach it, AI of the future will interact with humans the same way we naturally interact with each other, he said.

The initiative will build on Hopkins existing AI research at centers such as the Mathematical Institute for Data Science, Center for Imaging Science, and Laboratory for Computational Sensing and Robotics.

Computer vision and machine learning are transforming the way in which humans shop, share content, and interact with each other, said Ren Vidal, a professor and director of the Mathematical Institute for Data Science, in a statement.

This partnership will lead to new collaborations between JHU and Amazon scientists that will help translate cutting-edge advances in deep learning and visual recognition into algorithms that help humans interact with the world, he said.

More:
Johns Hopkins University teams up with Amazon to explore the power of artificial intelligence - - Baltimore Fishbowl

5 applications of Artificial Intelligence that are disrupting the banking sector – IBS Intelligence

5 applications of Artificial Intelligence that are disrupting the banking sector By Joy Dumasia

The adoption of AI in different enterprises has increased due to the COVID-19 pandemic. Since the pandemic hit the world, the potential value of AI has grown significantly. The focus of AI adoption is restricted to improving the efficiency of operations or the effectiveness of operations. However, AI is becoming increasingly important as organizations automate their day-to-day operations and understand the COVID-19 affected datasets. It can be leveraged to improve the stakeholder experience as well.

Artificial Intelligence (AI) has been around for a long time. AI was first conceptualized in 1955 as a branch of Computer Science and focused on the science of making intelligent machines machines that could mimic the cognitive abilities of the human mind, such as learning and problem-solving. AI is expected to have a disruptive effect on most industry sectors, many-fold compared to what the internet did over the last couple of decades. Organizations and governments around the world are diverting billions of dollars to fund research and pilot programs of applications of AI in solving real-world problems that current technology is not capable of addressing.

Artificial Intelligence enables banks to manage record-level high-speed data to receive valuable insights. Moreover, features such as digital payments, AI bots, and biometric fraud detection systems further lead to high-quality services for a broader customer base. Artificial Intelligence comprises a broad set of technologies, including, but are not limited to, Machine Learning, Natural Language Processing, Expert Systems, Vision, Speech, Planning, Robotics, etc.

The following are 5 applications of Artificial Intelligence that are disrupting the banking sector:

Automated advice is one of the most controversial topics in the financial services space. A robo-advisor attempts to understand a customers financial health by analyzing data shared by them and their financial history. Based on this analysis and goals set by the client, the robo-advisor will be able to give appropriate investment recommendations in a particular product class, even as specific as a specific product or equity.

Chatbots deliver a very high ROI in cost savings, making them one of the most commonly used applications of AI across industries. Chatbots can effectively tackle most commonly accessed tasks, such as balance inquiry, accessing mini statements, fund transfers, etc. This helps reduce the load from other channels such as contact centres, internet banking, etc.

AI is instrumental in helping alternate lenders determine the creditworthiness of clients by analyzing data from a wide range of traditional and non-traditional data sources. This helps lenders develop innovative lending systems backed by a robust credit scoring model, even for those individuals or entities with limited credit history. Notable companies include Affirm and GiniMachine.

One of AIs most common use cases includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which legacy technology could not previously detect. These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.

AI can significantly improve the effectiveness of cybersecurity systems by leveraging data from previous threats and learning the patterns and indicators that might seem unrelated to predict and prevent attacks. In addition to preventing external threats, AI can also monitor internal threats or breaches and suggest corrective actions, resulting in the prevention of data theft or abuse.

ALSO READ: Applications of Artificial Intelligence In Banking Q1 2022

Go here to see the original:
5 applications of Artificial Intelligence that are disrupting the banking sector - IBS Intelligence

SparkCognition Government Systems (SGS) Brings AI Readiness to the Department of Defense Through Joint Artificial Intelligence Center Award – PR…

SGS awarded Data Readiness Artificial Intelligence Development Program, a potential five-year $241.6M agreement for advancing use of AI in defense

AUSTIN, Texas, April 12, 2022 /PRNewswire/ --SparkCognition Government Systems (SGS), developing trusted artificial intelligence (AI) solutions for defense and national security, announced it has been awarded to the Data Readiness Artificial Intelligence Development (DRAID) Program through the Joint Artificial Intelligence Center (JAIC). The DRAID Program is a potential five-year, $241.6 million basic ordering agreement (BOA) focused on enabling the Department of Defense (DoD) to optimize its vast data resources to leverage AI to enhance its mission effectiveness.

"We are honored to have been selected for this critical initiative, building on our commitment to helping the DoD solve their most pressing challenges," said Logan Jones, President and General Manager of SGS. "Our leadership in AI, machine learning, natural language processing, and computer vision will be instrumental to our work with JAIC, advancing the DoDs use of diverse data sets and driving innovative AI applications across the department."

The goal of the DRAID program is to ensure DoD AI readiness, with a special focus on preparing data for building AI systems. As an awardee of the DRAID contract, SGS will apply its patented AI, machine learning, and natural language processing technologies to the challenges JAIC will focus on solving through the DRAID vehicle, including data cleanliness and readiness, and delivering data science techniques that lead to actionable insights. This ultimately enables the DoD to deploy solutions like SGS' offerings, which empower mission readiness, enhance decision making, and deliver efficient operations.

To learn more about SparkCognition Government Systems, visit http://www.sparkgov.ai.

About SparkCognition Government Systems SparkCognition Government Systems (SGS), a wholly-owned subsidiary of SparkCognition, is the first artificial intelligence (AI) company devoted entirely to government and national defense. By developing and operationalizing next-generation AI-powered systems, SGS enables government organizations to meet the needs of their most pressing national security missions. SGS advances government operations by analyzing complex data to inform and accelerate intelligent decisions, applying predictive and prescriptive analytics to improve logistics, deploying autonomy technology for power projection systems, using AI and machine learning for large-scale processing of unstructured data, and more. For in-depth information about SGS and its offerings, visit http://www.sparkgov.ai.

Contact Info Cara Schwartzkopf Communications Manager [emailprotected] 251-501-6121

SOURCE SparkCognition Government Systems

Visit link:
SparkCognition Government Systems (SGS) Brings AI Readiness to the Department of Defense Through Joint Artificial Intelligence Center Award - PR...

Artificial intelligence keeps the spirits up during the pandemic – The Australian Financial Review

The days when technology strategy was the sole preserve of IT specialists have long gone, and businesses are looking to have digital leaders across departments and dotted around the boardroom table.

However, that doesnt mean that business tech has become homogenous. There is significant competitive advantage that comes from doing things smarter than rival operators.

ADAPTs director of strategic research Matt Boon says well-established retailers are turning to tech solutions after their business models were upended by the pandemic.

It is always remarkable to me as I go around the world that companies are at different stages of digital transformation, in terms of their migration to the cloud, and in terms of their usage of data, CEO of global tech services giant Cognizant Brian Humphries tells The Financial Review.

In basic terms, every company in the world is trying to accelerate growth, protect themselves from next-generation companies, trying to upsell and cross-sell, and trying to maximise customer and employee satisfaction.

Big companies know that whether they are in insurance or healthcare, there are start-ups that are so disruptive, that they have to become much more digital if they want to survive.

Humphries was in Australia to visit local clients, including Telstra and Australia and New Zealand Banking Group, and says across industries, the emergence of easily accessible software-as-a-service, with AI-based functionality on tap means companies have adopted a fail fast culture, more associated with start-up disruptors.

Humphries says even organisations that have previously been tech laggards have had a change of attitude in recent years.

Ill probably have met 15 to 20 clients in Australia and New Zealand on my visit and every single one of them no longer thinks of IT as a cost, Humphries says.

They think of it as an investment and as core to the business; it is now the difference between winning and losing.

For a report entitled Embracing the Future: Top 12 Strategic Priorities for 2022, technology research firm ADAPT conducted a study based on interviews with over 650 senior Australian executives about their attitudes towards tech investment and how technology is being deployed.

Like Coles adoption of software to streamline and prioritise stock ordering, ADAPT found a desire to become data driven and improve operational effectiveness were the key aims of most of the execs surveyed.

Well-established retailers saw their business models upended by the pandemic, and have been compelled to re-imagine business functions and develop new product and service lines only possible through accelerated digitisation programs, ADAPTs director of strategic research Matt Boon says.

Those leading the pack now realise digital transformation isnt an end goal, but a constant state of flow needing executive support.

Hampering efforts across Australian businesses to achieve these aims of being data-driven, according to ADAPT, are a number of main hurdles. First, data often resides across disparate systems and applications, making it hard to analyse effectively.

There is also a general lack of sufficient data skills in many organisations, a lack of executive ownership from non-technology focused business leaders and insufficient budget dedicated to making it happen.

Boon says that while 60 per cent of Australian IT leaders have indicated a focus on developing IT culture in their organisations, ADAPT considers just 43 per cent of C-suite executives and board directors to be digitally savvy.

Companies need to adopt emerging technologies, but are being held back by low levels of digital literacy from the boardroom all the way down through an organisation, Boon says.

IT leaders are right to prioritise the development of their organisations digital savviness, as the success of new initiatives in the future depends upon their teams willingness to support them.

While a broader shift of systems on to cloud-hosted infrastructure, and the installation of myriad systems to try and protect against cyber security threats are responsible for a significant portion of tech budgets across all companies, ADAPTs study shines a light on the relatively nascent technologies also being trialled.

Machine learning, artificial intelligence and robotic process automation are the most common of these areas to be attracting funding, ahead of chatbots, blockchain, the internet of things (IoT) and virtual or augmented reality.

Australian companies including Lendlease and Newcrest Mining have both spoken in recent times about how the combination of IoT sensors with cloud-based systems and virtual and augmented reality have allowed them to reap significant benefits from building digital twins.

These virtual representations of real-world conditions at their various assets, such as building sites or gold mines, can be analysed from the safety of an office, with potentially lucrative benefits.

Newcrest estimates that a digital twin of its flagship Cadia mine in NSW could unlock hundreds of millions of dollars of untapped value a year, through more efficient maintenance and enabling the mining of more gold.

Mikko Krkkinen, the CEO and co-founder of Relex Solutions, the software firm helping Coles get the right drinks on its liquor store shelves, says that while his company focuses on the retail sector, the focus on supply chain planning and the use of AI and machine learning-based technology is relevant across industries.

Were seeing a need for retailers to be able to analyse and integrate unprecedented amounts of data across various parts of their business, and we know AI to be a powerful tool for optimising wider parts of the business, Krkkinen says.

The pandemic made retailers, along with everyone else, realise how unpredictable the world has become.

Its impossible to predict every twist and turn the market will take in the future, whether near or distant. Companies committed to long-term future success understand this and invest in solutions that help them remain agile and adaptive in their approach and plans, regardless of how the market evolves going forward.

Top 10 business priorities:

(Source: ADAPTs Embracing the Future: Top 12 Strategic Priorities for 2022 report)

Top challenges to technology initiatives

(Source: ADAPTs Embracing the Future: Top 12 Strategic Priorities for 2022 report)

Link:
Artificial intelligence keeps the spirits up during the pandemic - The Australian Financial Review

Is artificial intelligence the future of writing? – The Rude Baguette

Its not new that the emerging artificial intelligence technology aims to take over the writing space.

High-end and intermediate writers have expressed cynical views and even fears over the AI writing software introduction.

For proponents of the AI writing application, its not so! According to them, the concept behind the creation is to help lessen the workload of writers.

In the meantime, the number of AIs has surpassed expectations. From small companies to big names in tech, AIs are attempting to become the next big thing for content marketing.

In fact, due to the improvement in its machine language and data analytics, some companies prefer AI content marketing.

This begs the question, is AI the future of writing? Or will it replace the human writing form?

Read on!

If youve been wondering what goes on behind every AI, its simple, a machine language.

AI writing tools usenatural language generationto produce written words from mere data. You just input data in, and the rest is history.

An AI is effective when a large amount of data needs conversion into written language that anyone can understand.

Scientists didnt stop at a mere natural language generation; more work began after the discovery in 2016.

They rebranded and created a more advanced AI that didnt need data labeling while saving time and money.

In May 2020, another model was created. Its called OpenAIs GPT-3 (Generative Pre-trained Transformer-3).

This new and advanced machine language is the largest neural network globally. The machine has a model with over 175 million parameters.

The GPT-3 is different from other AIs because it processes information like the human thinking faculty.

It executes tasks like answering questions, filling in blanks, publishing articles, writing songs, jokes, and even questions about the philosophical aspect of life!

There are even better and more advanced ALs being created. In particular, some companies have copied the language system of the OpenAIsGTP-3and made better improvements.

In May 2020, Google launched a new chatbot called LAMDA. Its designed to hold meaningful, emotional, and intellectual conversations.

Whats more, Beijing has attempted to create the first living AI. In June 2020, the Beijing Academy of Artificial Intelligence (BAAI) launched a new AI calledWu Dao 2.0.

The AI gave life to its first virtual student,Hua Zhubingto write songs and codes and possess a large memory.

This has become a lingering question in every writers mind and probably a writers worst fear, especially writers in the business ofcontent writing or copywriting.

While AI technology keeps advancing, its arguably not going to be the future of writing.

Writers are more skilled in capturing the essence and reader perception. Itll take years of research for any AI to exhibit such traits. An AI cant write emphatically as a human would.

Although AI has shown great dexterity and expertise in writing, there are still major gaps that cant be filled.

Below are a few reasons why writers need not worry about AIs for now:

An AI lacks the uniqueness human writers bring to their articles. Its an intricate factor that distinguishes the pro from the amateur.

AIs may be perfect for data gathering and analyzing complex words but possess poor creative analytics.

They poorly express themselves due to a lack of cognition and emotion. Only humans can process such complexities.

AIs produce whatever you run into them. The process is like garbage in garbage out.

The workload still falls on a human to carefully reread and edit AI-generated articles.

Yes, it might be difficult to detect an AI-written article. However, AIs struggle to compose coherent and engaging content to captivate readers. Engagement is the footstone of every good content.

Writers are more skilled in capturing the essence of every article. It may take years of research for AIs to exhibit such traits.

If theres one thing an AI greatly lacks in information presentation, its a lack of direct and multiple evaluations.

For instance, an AI cant interpret a proverb or an idiom. They arent recognizable in data analysis.

Also, they cant differentiate between the linguistic complexities, like when not to use offensive words.

For now, human writers have nothing to worry about. AIs and humans can coexist symbiotically without one dominating the other.

Though many believe its economical and more reliable than human writers. However, the barrier to the above statement is the cost of an AI to start up. Only big tech companies can afford excellent and effective AI writing tools.

The risk-on human writers are quite low. However, it shouldnt stop you from honing your skill!

Sam Altman, CEO of Open AIs, in a tweet published in early June 2021, stated that AIs might likely affect physical jobs more than remote jobs such as coding, writing, administrative jobs, and co.

Whether we like it or not, AIs are here to stay. We cant fight them. However, we can create a means to incorporate them into the physical fold without any job losses.

They immensely contribute to accelerating a writers process and simplifying the workload.

We already use low-resource AIs like Grammarly and plagiarism checkers. Still, human editors and proofreaders are thriving.

Photo by Reports Monitor from Flickr

Read more from the original source:
Is artificial intelligence the future of writing? - The Rude Baguette

New York Citys New Law Regulating the Use of Artificial Intelligence in Employment Decisions – JD Supra

On Nov. 10, 2021, the New York City Council passed a bill that regulates employers and employment agencies use of automated employment decision tools in making employment decisions. The bill was returned without Mayor Bill de Blasios signature and lapsed into law on Dec. 11, 2021. The new law takes effect on Jan. 1, 2023. This new law is part of a growing trend towards examining and regulating the use of artificial intelligence (AI) in hiring, promotional and other employment decisions.

Requirements of the New Law. The new law regulates employers and employment agencies use of automated employment decision tools on candidates and employees residing in New York City. An automated employment decision tool refers to any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output, including a score, classification, or recommendation, that is used to substantially assist or replace discretionary decision making for making employment decisions that impact natural persons.

The new law prohibits an employer or employment agency from using an automated employment decision tool in making an employment decision unless, prior to using the tool, the following requirements are met: (1) the tool has been subject to a bias audit within the last year; and (2) a summary of the results of the most recent bias audit and distribution data for the tool have been made publicly available on the employer or employment agencys website. A bias audit is defined as an impartial evaluation by an independent auditor, which includes the testing of an automated employment decision tool to assess the tools disparate impact on persons of any component 1 category required to be reported by employers pursuant to 42 U.S.C. 2000e-8(c) and 29 C.F.R. 1602.7.

The new law also requires employers and employment agencies to satisfy two notice requirements. First, at least 10 business days before using the tool, the employer or employment agency must notify a candidate or employee who resides in New York City of the following: (1) that an automated employment decision tool will be used in assessing the candidate or employee; and (2) the job qualifications and characteristics that the tool will use in the assessment. The employer or employment agency must allow the candidate or employee to request an alternative process or accommodation. However, the law is silent as to the employer or employment agencys obligation to provide such alternative process or accommodation. Second, the employer or employment agency must disclose on their website or make available to a candidate or employee within 30 days of receiving a written request, the following: (1) information about the type of data collected for the automated employment decision tool; (2) the source of the collected data; and (3) the employer or employment agencys data retention policy.

Penalties for Violations. Violations of the new law will result in liability for a civil penalty of up to $500 for the first violation and each additional violation occurring on the same day as the first violation, and a civil penalty between $500 and $1,500 for each subsequent violation. Importantly, each day the automated employment decision tool is used in violation of the law constitutes a separate violation and the failure to provide any of the required notices constitutes a separate violation.

Recommendations for Timely Compliance. Employers with candidates or employees who reside in New York City can take several steps now to facilitate compliance with this new requirement when it goes into effect on Jan. 1, 2023. Employers should ensure that any covered automated employment decision tool that they plan to use in 2023 or thereafter to assess New York City candidates and employees is subject to a bias audit by an independent auditor and the results of such audit are available on their website. Additionally, we recommend that employers and employment agencies work with their legal counsel to develop and implement practices that comply with the notice provisions required by the new law.

Other Regulations on Automated Employment Decision Tools. Several states and cities have passed or are considering similar laws regarding the use of artificial intelligence and other technology in employment decisions. For example, Illinois Artificial Intelligence Video Interview Act, which took effect Jan. 1, 2020, requires employers using AI interview technology to provide advanced notice and an explanation of the technology to applicants, to obtain the applicants consent to use the technology and to comply with restrictions on the distribution and retention of videos. Similarly, Maryland enacted a law that took effect Oct. 1, 2020, which requires employers to obtain an applicants written consent and a waiver prior to using facial recognition technology during pre-employment job interviews. California and Washington, D.C. have also proposed legislation that would address the use of AI in the employment context.

Additionally, on Oct. 28, 2021, the U.S. Equal Employment Opportunity Commission (EEOC) launched a new initiative aimed at ensuring artificial intelligence and other technological tools used in making employment decisions comply with the federal civil rights laws. As part of its initiative, the EEOC will gather information about the adoption, design and impact of employment-related technologies, and issue technical assistance to provide employers with guidance on algorithmic fairness and the use of artificial intelligence in employment decisions.

[View source.]

See the original post here:
New York Citys New Law Regulating the Use of Artificial Intelligence in Employment Decisions - JD Supra

Artificial intelligence can spot the signs of PTSD in your text messages – Study Finds

EDMONTON, Alberta A text message may be able to reveal if someone is dealing with post-traumatic stress disorder (PTSD), a new study finds. Researchers from the University of Alberta say a machine learning program a form of artificial intelligence is capable of reading between the lines to find potential warning signs in the way people write.

The team believes this program could become an inexpensive tool that helps mental health professionals detect and diagnose cases of PTSD or other disorders. Psychiatry PhD candidate Jeff Sawalha performed a sentiment analysis of texts using a dataset created by Jonathan Gratch from USCs Institute for Creative Technologies.

Study authors explain that a sentiment analysis takes a large amount of data and categorizes it. In this case, the model took a massive amount of texts and sorted them according to positive and negative thoughts.

We wanted to strictly look at the sentiment analysis from this dataset to see if we could properly identify individuals with PTSD just using the emotional content of these interviews, Sawalha says in a university release.

The text sampling came from 250 semi-structured interviews conducted by an artificial interviewer (Ellie) who spoke with real participants using video conferencing calls. Eighty-seven people had PTSD while the other 188 did not.

From their text responses, the team was able to identify people with PTSD through their scores reflecting how often their words displayed neutral or negative thoughts.

This is in line with a lot of the literature around emotion and PTSD. Some people tend to be neutral, numbing their emotions and maybe not saying too much. And then there are others who express their negative emotions, Sawalha says.

Study authors note that this process isnt black and white. For example, a phrase like I didnt hate that could be confusing for the algorithm. Despite that, the machine learning system was able to detect PTSD patients with 80 percent accuracy.

Text data is so ubiquitous, its so available, you have so much of it, Sawalha continues. From a machine learning perspective, with this much data, it may be better able to learn some of the intricate patterns that help differentiate people who have a particular mental illness.

The team is planning to integrate other types of data, including speech patterns and human motions, which they say may help the system spot mental health disorders better. Moreover, signs of neurological conditions like Alzheimers disease are detectable through a persons ability to speak.

Unlike an MRI that takes an experienced person to look at it, this is something people can do themselves. I think thats the direction medicine is probably going, toward more screening tools, says Russ Greiner, a professor in the Department of Computing Science.

Having tools like this going forward could be beneficial in a post-pandemic world, Sawalha concludes.

The study is published in the journal Frontiers in Psychiatry.

Go here to read the rest:
Artificial intelligence can spot the signs of PTSD in your text messages - Study Finds

Lakeland’s busiest intersections will get artificial intelligence sensors to prevent crashes – ABC Action News Tampa Bay

LAKELAND, Fla. Lakeland is using artificial intelligence to reduce the number of deadly car crashes.

Weve all seen that driver who speeds into the intersection trying to beat the red light. The City of Lakeland is using artificial intelligence so that traffic signals can identify reckless drivers before they cause a crash.

Vehicles caught running through red lights in Lakeland

It will tell us as a car is approaching the intersection, the likelihood of it stopping, said Jeff Weatherford, traffic operations manager for the City of Lakeland.

Lakeland's Intersection Collision Avoidance Safety Program, or iCASP works by delaying the green light of cross-traffic up to four seconds, when sensors detect a vehicle is going to run a red light.

There are actually several sensors, but the furthest one out is about 150 feet from the stop line, Weatherford said.

The Florida Department of Transportation is investing $500,000 to expand the citys red light-running detection program, to 25 high-impact intersections. Lakeland has already been operating the red light running sensors at four major intersections, in a pilot program since last February. The data shows a significant amount of red-light runners putting the lives of other drivers at risk.

In a 24-hour period, there were 45 cars that ran the red light, were in the intersection while the light was red, at that intersection headed eastbound and we had zero crashes that day, Weatherford said.

The smart system makes a split-second decision, potentially avoiding deadly collisions.

The city is also looking at incorporating a new mobile app with iCASP to alert drivers of a red-light runner.

Phone apps that will run in the background, being able to send out an alert to a driver that will warn them, caution, watch for crossing traffic,' said Weatherford.

Read the rest here:
Lakeland's busiest intersections will get artificial intelligence sensors to prevent crashes - ABC Action News Tampa Bay