Table of Contents
- Asana launches prediction-based AI tools to improve decision making
- Renowned technologists and entrepreneurs Neha Narkhede and Sachin Kulkarni announce launch of Oscilar – the first company that uses real-time data and AI to protect every online transaction
- Artificial Intelligence For IT Operations Platform Market to Gain Huge Growth at 17.7% CAGR During the Forecast Period 2022-2030
- Chinese tech giant Baidu just released its answer to ChatGPT
- LinkedIn’s AI writing assistant can help write your profile
- Don’t overlook independence in Responsible AI
- Tavus taps generative AI to power personalized videos with voice and face cloning
- ChatGPT Stats
- 5 Steps to Build an AI Powered Semantic Search Application using Cohere’s API
- DataRobot releases AI Platform 9.0 to deliver value-driven AI
- Explainer: What Is Generative AI, The Technology Behind OpenAI’s ChatGPT?
- Explainer: What is Generative AI, the technology behind OpenAI’s ChatGPT?
- OpenAI Unveils Multimodal LLM GPT-4: The Most Advanced AI Yet
- Meet the Winners of ‘Women in AI Leadership Awards’ at The Rising 2023
- NVIDIA Expands Omniverse Cloud to Power Industrial Digitalization
- “Conversational everything” for customer communications is growing
- KANBAN TOOL LAUNCHES ITS AI ASSISTANT TO MAKE PROJECT MANAGEMENT SMARTER AND MORE EFFICIENT
- Vectra Unifies AI-driven Behavior-based Detection and Signature-based Detection in a Single Solution
- Ubisoft Introduces Its Own AI That Writes Future Dialogues
- WHY MANY BUSINESSES ARE FAILING AT MACHINE LEARNING
Asana launches prediction-based AI tools to improve decision making
Charlotte Trueman
Computer World
Work management platform Asana has announced three new features designed to provide greater cross-organizational transparency and improved insights into how employees are collaborating.
The new features have been categorized by Asana as decision intelligence, resource intelligence, and execution intelligence and include a new dashboard that provides greater insights into key metrics, a universal workload tool that uses predictive AI to help team leaders make better resourcing decisions, and new workflow bundles that scale workflow updates across entire organizations.
sana has added new executive reporting with portfolio dashboards and roll-ups for key metrics like budget and time will be available to enterprise and business customers, providing leaders with a view of the ROI of their investments, as well as the health and status of strategic initiatives, capacity, and budgets.
Asana is also launching a universal workload tool, in order to help organizations make better resourcing decisions.
It provides leaders with a holistic view of team capacity across all projects and programs throughout their entire organization.
Asana has chosen to focus more on the prediction aspect of AI rather than its content-generating capabilities because as a company, it wants to leverage the technology where it makes the most sense for its customers, Hood said.
Link: https://www.computerworld.com/article/3691620/asana-launches-prediction-based-ai-tools-to-improve-decision-making.html?utm_date=20230329132709&utm_campaign=Computerworld%20US%20First%20Look&utm_conten
Renowned technologists and entrepreneurs Neha Narkhede and Sachin Kulkarni announce launch of Oscilar – the first company that uses real-time data and AI to protect every online transaction
Cision PR Web
SAN FRANCISCO, March 30, 2023 /PRNewswire/ — Confluent (NASDAQ: CFLT) Co-Founder and Board Member Neha Narkhede and former Meta engineering executive Sachin Kulkarni today announced the launch of their newest company, Oscilar, with the goal of solving one of the biggest challenges facing businesses and consumers: how to protect online transactions from fraud and theft.
Oscilar is a first-of-its kind real-time Artificial Intelligence (AI) powered platform that, within milliseconds, makes online transactions significantly safer for companies and consumers.
Oscilar’s all-new approach to fraud and risk management, called AI Risk Decisioning, is the most advanced solution ever created to protect online transactions.
Link: https://www.prnewswire.com/news-releases/renowned-technologists-and-entrepreneurs-neha-narkhede-and-sachin-kulkarni-announce-launch-of-oscilar–the-first-company-that-uses-real-time-data-and-ai-to-pro
Artificial Intelligence For IT Operations Platform Market to Gain Huge Growth at 17.7% CAGR During the Forecast Period 2022-2030
Market Reports Assistance
The global artificial intelligence for IT operations platform market size was valued at USD 7.1 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 17.7% from 2022 to 2030.
Artificial intelligence for IT operations (AIOps) platform refers to the AI platform for IT operations.
It combines human intelligence with automated algorithms to provide full visibility into the performance of IT systems.
The key factor driving the adoption of AIOps in the IT environment is the fulfillment of adequate speed and agility, required for the business.
Recent technological advancements have paved the way for AI in IT operations.
Several companies are adopting the connection of knowledge, Natural Language Processing (NLP), and domain-enriched Machine Learning (ML) techniques, to offer improved AIOps platforms and services.
AIOps platform uses intelligence and self-learning algorithms, backed by ML to automate regular IT tasks.
It also detects and anticipates any possible events via historical and behavioral data analysis.
Moreover, it provides a cognitive analysis of data by leveraging big data analytics and derives meaningful information from data for comprehensive processing.
The integration of IT operations with AI ensures real-time data correlation, multi-dimensional data normalization, severity-based incident prioritizing, and predefined response plans to mitigate future events.
This ability to derive actionable insights from scratch data helps build a responsive ITOps infrastructure.
For instance, in March 2022, TSB Bank plc, a retail and commercial bank based in the United Kingdom, is leveraging the Dynatrace platform to speed up innovation as it extends digital offerings for its clients.
Link: https://marketreportsassistance.wordpress.com/2023/03/16/artificial-intelligence-for-it-operations-platform-market-to-gain-huge-growth-at-17-7-cagr-during-the-forecast-period-2022-2030/
Chinese tech giant Baidu just released its answer to ChatGPT
Zeyi Yang
Technology Review
On March 16, Robin Li, Baidu’s cofounder and CEO, took the stage in Beijing to showcase the company’s new large language model, Ernie Bot.
Accompanied by art created by Baidu’s image-making AI, he showed examples of what the chatbot can do, including solve math questions, write marketing copy, answer questions about Chinese literature, and generate multimedia responses.
Baidu had planned for this mid-March product release for months.
But it was intercepted by the unexpected release on Tuesday of OpenAI’s GPT-4, which clearly became a reference point for everyone watching Baidu’s activities, including the CEO himself. “People are expecting to benchmark Ernie Bot against ChatGPT, or even GPT-4.
That’s a very high bar,” Li said at the beginning of his presentation.
As expected, Ernie Bot (the name stands for “Enhanced Representation from kNowledge IntEgration;” its Chinese name is 文心一言, or Wenxin Yiyan) performs particularly well on tasks specific to Chinese culture, like explaining a historical fact or writing a traditional poem. (Li says as a Chinese company, Baidu “has to perform better than any pre-trained LLMs” in terms of understanding Chinese.)
But the highlight of the product release was Ernie Bot’s multimodal output feature, which ChatGPT and GPT-4 do not offer (OpenAI has bragged about GPT-4’s ability to analyze a photo of the contents of a refrigerator and come up with recipe suggestions, but the model generates only text).
Link: https://www.technologyreview.com/2023/03/16/1069919/baidu-ernie-bot-chatgpt-launch/
LinkedIn’s AI writing assistant can help write your profile
Saqib Shah
Evening Standard
LinkedIn describes the AI as a “personalised writing” tool that can use the content already on your profile to make suggestions that can help you stand out.
Though it’s starting with profile-writing suggestions, LinkedIn could feasibly expand the tool to assist with CVs and cover letters – if all goes smoothly, that is.
Alongside the writing assistant, LinkedIn is also giving recruiters an AI feature that can whip up job descriptions with just a smattering of info, like job title, company name, workplace type, job type, and location.
The company says the tool is powered by an advanced GPT model from OpenAI, the firm responsible for the ChatGPT chatbot.
The updates are part of LinkedIn parent company Microsoft’s big push into AI, and follow the launch of its Bing AI chatbot, AI-powered LinkedIn posts (or “conversation starters”), and upcoming features for Office apps.
Link: https://www.standard.co.uk/tech/linkedin-ai-writing-assistant-can-help-write-your-profile-b1068197.html
Don’t overlook independence in Responsible AI
Dr Stuart Battersby
Inside Big Data
Whilst the field of Responsible AI is gaining traction in the enterprise (in part driven by imminent regulation such as the EU’s AI Act), there are issues with current approaches to implementing Responsible AI.
Possibly to due illiteracy in AI and data across large organizations, the task of Responsible AI is often thrown to the data science teams.
These teams are usually made up of scientists who are tasked with designing and building effective and accurate AI models (most often using machine learning techniques).
The key point here is that it’s not the right approach to task the teams (and by association, the technologies they use) that build the models, with the job of objectively evaluating these models.
As required by the Securities and Exchange Commission (SEC) in the United States, the auditor of a company’s finances must be fully independent from the company in question.
Independence is also a key requirement in the Model Risk Management (MRM) process – a process by which the statistical models developed in financial institutions are independently tested and verified.
The three levels of MRM (Model Development, Model Validation and Internal Audit) should each maintain strict independence from each other.
Independence in Responsible AI should apply to both the people carrying out the assessments and the technology that they use to do it.
Link: https://insidebigdata.com/2023/03/17/dont-overlook-independence-in-responsible-ai/
Tavus taps generative AI to power personalized videos with voice and face cloning
Planet Newspost
Founded out of San Francisco in 2020 by CEO Hassaan Raza and Quinn Favret, Y Combinator (YC) alum Tavus today announced that it has raised $6.1 million in a seed round of funding led by Silicon Valley investor Sequoia, with participation from a slew of high-profile backers including Accel Partners, Index Ventures, Lightspeed Ventures, and YC Continuity.
Any company looking to create multiple personalized videos will know that it’s an incredibly time-consuming, repetitive process: recording the same message, with substantively the same content, but tweaked for different clients or candidates.
And that is what Tavus is looking to address, allowing users to create their own AI video templates in minutes, and then generate an unlimited number of versions of a video from that original source.
The initial onboarding process requires the user — for example, a recruiter or sales executive — to record a 15-minute video based on a script provided by Tavus, which is used to train the AI.
Then, the user records a template for each campaign they want to create.
Using a web-based editor, users can then select what elements of the video they want to personalize, specifying each variable (e.g. company, executive name or location), adding in calls to action, and so on.
At the top end of the generative AI spectrum, we’re seeing the likes of Microsoft and Google slug it out to see who can get their respective smarts into the hands of businesses and consumers the quickest, a battle that Microsoft seems to be winning at present.
At the same time, we’re seeing a whole host of generative AI startups come to the fore such as GlossAi, which is using AI to help businesses easily create shareable marketing skits, while Typeface is doing something similar for marketing copy and image-generation in the enterprise.
Specific to Tavus, there have been comparable companies out there for a few years already such as Windsor, which does something similar albeit with a heavy focus on ecommerce.
And then there is London-based Synthesia, backed by a swathe of high-profile investors, which is more about creating digital avatars from text for use in training and how-to videos.
Prior to this latest seed round, Tavus had raised a small amount of funding as part of its participation in the YC program back in 2021.
Its full roster of seed-round investors include: Sequoia, Accel Partners, Index Ventures, Lightspeed Ventures, YC Continuity, SV Angel, Hack VC, Remus Capital, Mantis Capital, Liquid2 Ventures, Zillionize, Soma Capital, GTMfund, Terra Nova, and several undisclosed angel investors.
Link: https://planetnewspost.com/tech/tavus-taps-generative-ai-to-power-personalized-videos-with-voice-and-face-cloning/
ChatGPT Stats
Jamie Spencer
Make a Web Site Hub
Time to first 1 million users
ChatGPT – 5 days
Facebook – 10 months
Instagram – 2 months
Spotify – 5 months
Netflix – 3.5 years
Monthly Total Visits
February 2023 1 Billion
January 2023 616 Million
December 2022 266 Million
According to Sam Altman, CEO of OpenAI, the fee for a single chat with ChatGPT is probably lower than 10 cents.
According to Tom Goldstein, an AI ML Professor at Maryland University, the daily cost of powering ChatGPT is around $100,000 and its monthly expenditure stands at a whopping 3 million USD.
His calculations are based on Azure Cloud costs (which is where ChatGPT runs).
He further added that the model has 175 billion parameters while it receives 10 million queries per day (as seen on his Twitter page).
For ChatGPT Plus users, Open AI has introduced “Turbo” mode to ensure faster response times and greater satisfaction.
ChatGPT could make $200 million dollars by the end of 2023 and $1 billion dollars by the end of 2024. (Reuters).
ChatGPT has been developed using Reinforcement Learning from Human Feedback (RLHF), a revolutionary Large Language Model (LLM) that is trained by human feedback.
ChatGPT Cannot Access the Internet.
As an AI language model, ChatGPT cannot access the internet or external links; it can only respond to questions and supply information based on its training material.
ChatGPT’s knowledge is limited to its training data which has the cutoff year of 2021.
Alternatives & Competitors of ChatGPT
Google Vs ChatGPT
Microsoft has been a step ahead of Google in launching an AI search assistant.
Several users have already been able to preview Bing AI “the co-pilot for the web”.
See examples of Bing AI’s responses.
Which model is behind Bing AI?
OpenAI made a new kind of language model.
It is better than ChatGPT and GPT-3.5.
It works faster, is more accurate, and can do more things.
This has since been confirmed to be GPT-4.
Unlike ChatGPT, Youchat is connected to the internet and can consequently engage in conversations with its users – just like ChatGPT.
ChatGPT has a pitfall of presenting statistics and definitions without attributing them to any source, but thankfully Wordtune is here with its conversational model that offers citations.
Link: https://makeawebsitehub.com/chatgpt-stats/
5 Steps to Build an AI Powered Semantic Search Application using Cohere’s API
Elle Neal
Medium
In this article, I will introduce Cofinder, a semantic search application built using Cohere’s technology.
I’ll provide you with a repository and explanations of the code snippets to help you follow along and build your own semantic search application.
We will go through the 5 stages of building your application:
Pre-processing your data sources
Creating a search index
Building your Streamlit front end
Adding an AI search function
Adding AI generated answer functions.
In recent years, natural language processing (NLP) has rapidly advanced, thanks to the latest generation of large language models.
However, not all developers are able to use these models due to the high compute barriers and the lack of technical expertise required to do so.
This is where Cohere comes in.
Cohere provide access for all developers, without the need for ML expertise, all that’s needed to access llm via cohere is a simple API call to large language models that can be used to generate or analyse text to do things like write copy, moderate content, classify data and extract information, all at a massive scale.
Cohere offer a generous free developer tier, meaning you can build and test your ideas quickly and with no cost.
Link: https://medium.com/@elle.neal_71064/5-steps-to-build-an-ai-powered-semantic-search-application-using-coheres-api-f5a60cb797be
DataRobot releases AI Platform 9.0 to deliver value-driven AI
Help Net Security
DataRobot has released DataRobot AI Platform 9.0, along with deeper partner integrations, AI Accelerators, and redesigned service offerings, all centered on helping organizations derive measurable value from their AI investments.
The breakthrough innovations inside the DataRobot AI Platform include capabilities that facilitate:
Rapid experimentation and value identification using Workbench, DataRobot’s brand new collaborative experimentation experience.
Reduced enterprise risk and barriers to production through well-architected guard rails — from bias mitigation, centralized model monitoring, to automated model compliance documentation of both DataRobot and non-DataRobot models.
Tremendous value, right from the outset with AI Accelerators and new AI services packages that provide customers with years of DataRobot’s AI expertise to jumpstart AI projects and results.
Link: https://www.helpnetsecurity.com/2023/03/18/datarobot-ai-platform-9-0/
Explainer: What Is Generative AI, The Technology Behind OpenAI’s ChatGPT?
Zee News
Like other forms of artificial intelligence, generative AI learns how to take actions from past data.
It creates brand new content – a text, an image, even computer code – based on that training, instead of simply categorizing or identifying data like other AI.
GPT-4, a newer model that OpenAI announced this week, is “multimodal” because it can perceive not only text but images as well.
OpenAI’s president demonstrated on Tuesday how it could take a photo of a hand-drawn mock-up for a website he wanted to build, and from that generate a real one.
WHAT IS IT GOOD FOR?
The technology is helpful for creating a first-draft of marketing copy, for instance, though it may require cleanup because it isn’t perfect.
One example is from CarMax Inc (KMX.N), which has used a version of OpenAI’s technology to summarize thousands of customer reviews and help shoppers decide what used car to buy.
IS THIS JUST ABOUT GOOGLE AND MICROSOFT?
Large companies like Salesforce Inc (CRM.N) as well as smaller ones like Adept AI Labs are either creating their own competing AI or packaging technology from others to give users new powers through software.
HOW IS ELON MUSK INVOLVED?
He was one of the co-founders of OpenAI along with Sam Altman.
But the billionaire left the startup’s board in 2018 to avoid a conflict of interest between OpenAI’s work and the AI research being done by Telsa Inc (TSLA.O) – the electric-vehicle maker he leads.
Link: https://zeenews.india.com/technology/explainer-what-is-generative-ai-the-technology-behind-openais-chatgpt-2584773.html
Explainer: What is Generative AI, the technology behind OpenAI’s ChatGPT?
Reuters
Like other forms of artificial intelligence, generative AI learns how to take actions from past data.
It creates brand new content – a text, an image, even computer code – based on that training, instead of simply categorizing or identifying data like other AI.
GPT-4, a newer model that OpenAI announced this week, is “multimodal” because it can perceive not only text but images as well.
OpenAI’s president demonstrated on Tuesday how it could take a photo of a hand-drawn mock-up for a website he wanted to build, and from that generate a real one.
WHAT IS IT GOOD FOR?
The technology is helpful for creating a first-draft of marketing copy, for instance, though it may require cleanup because it isn’t perfect.
One example is from CarMax Inc (KMX.N), which has used a version of OpenAI’s technology to summarize thousands of customer reviews and help shoppers decide what used car to buy.
IS THIS JUST ABOUT GOOGLE AND MICROSOFT?
Large companies like Salesforce Inc (CRM.N) as well as smaller ones like Adept AI Labs are either creating their own competing AI or packaging technology from others to give users new powers through software.
HOW IS ELON MUSK INVOLVED?
He was one of the co-founders of OpenAI along with Sam Altman.
But the billionaire left the startup’s board in 2018 to avoid a conflict of interest between OpenAI’s work and the AI research being done by Telsa Inc (TSLA.O) – the electric-vehicle maker he leads.
Link: https://www.reuters.com/technology/what-is-generative-ai-technology-behind-openais-chatgpt-2023-03-17/
OpenAI Unveils Multimodal LLM GPT-4: The Most Advanced AI Yet
Alex Mcfarland
Unite.AI
Chat GPT-4 is a truly multimodal language model, with the ability to respond to both text and images.
Its capability to understand and generate responses based on visual inputs has significant implications for various industries.
As an example of its true power, GPT-4 can suggest recipes based on a photo of the contents of a fridge.
The model is 82% less likely to respond to requests for content that OpenAI does not allow and 60% less likely to generate false or inaccurate information.
This has been achieved through the use of reinforcement learning via human feedback.
The model has also been designed to learn from its own mistakes.
The team used GPT-4 to generate biased, inaccurate, or offensive responses, then fixed the model so that it refused such inputs in future.
As with any machine learning model, Chat GPT-4’s responses are only as unbiased as the data it is trained on.
Another concern is the potential for Chat GPT-4 to generate false or misleading information.
Privacy is also a concern with Chat GPT-4, as the model’s ability to generate responses based on user inputs raises questions about data privacy and ownership.
As users interact with the model and provide it with personal information, there is a risk that this data could be collected and used without their knowledge or consent.
Finally, there are concerns about job displacement.
Link: https://www.unite.ai/openai-unveils-multimodal-llm-gpt-4-the-most-advanced-ai-yet/
Meet the Winners of ‘Women in AI Leadership Awards’ at The Rising 2023
Poulomi Chatterjee
Analytics India Magazine
Anjali Iyer, Senior Delivery Practice Leader at TheMathCompany Inc
Debarati Sengupta, Chief Product Officer, Futurense Technologies
Divya Gupta, Country IT Lead, Dyson
Jayashree Arunkumar, Engineering Head, Wipro Digital
Kavitha Krishnan M, Senior Manager of Data Science, Tredence
Khushboo Singhal, Director of Insurance Analytics, WNS Global Services
Megha Sinha, Vice President, Genpact India
Monali Bhalerao, Director of Engineering, Eaton India Innovation Center
Neha Singh, Client Partner, Fractal Analytics
Priya Kanduri, CTO & Senior Vice President, Happiest Minds Technologies
Rashmi Ramesh, Functional Head and Principal Engineer, Société Générale Global Solution Centre
Reji Rajendran Nair, Associate Director for Cloud and DevOps Platform Engineering, Verizon India
Ruma Mukherjee, Principal Engineer, Intel
Safala V Revankar, Associate Director of Cloud Area Practice, Kyndryl
Seema Ramachandra, Head of Customer Engineering for Data, Analytics & AI, Google India
Shanthi Srinivasan, AVP and Head of Marketing, InfoCepts
Sindhu Ravindranathan, Global Director, Analytics, Unilever Industries
Sneha Utturwar, Program Director, Saama Technologies
Soumya Sethuraman, Director of Analytics Consulting, Tiger Analytics
Susmita Chaudhury, Partner, Deloitte India Consulting
Link: https://analyticsindiamag.com/meet-the-winners-of-women-in-ai-leadership-awards-at-the-rising-2023/
NVIDIA Expands Omniverse Cloud to Power Industrial Digitalization
CIO Influence
New Platform-as-a-Service Coming Soon to Microsoft Azure, Follows Initial Omniverse Adoption by BMW Group, Geely Lotus and Jaguar Land Rover
NVIDIA announced that NVIDIA Omniverse Cloud, a platform-as-a-service that enables companies to unify digitalization across their core product and business processes, is now available to select enterprises.
The new subscription offering for Omniverse Cloud on Azure makes it easy for automotive teams — from design and engineering to smart factory to marketing — to digitalize their workflows, whether connecting 3D design tools to accelerate vehicle development, building digital twins of automotive factories or running closed-loop simulations to test vehicle performance.
Powered by NVIDIA OVX computing systems, Omniverse Cloud enables enterprise developers to customize foundation applications that are included with the platform-as-a-service:
Omniverse USD Composer (formerly Omniverse Create)
Omniverse USD-GDN Publisher
NVIDIA Isaac Sim™
NVIDIA DRIVE Sim™
Omniverse Replicator
Link: https://cioinfluence.com/computing/nvidia-expands-omniverse-cloud-to-power-industrial-digitalization/
“Conversational everything” for customer communications is growing
Social Barrel
The data, gathered from 449 billion communications exchanges on Infobip‘s platform between 2022 and 2049, demonstrates the explosive expansion of communications on social media platforms like Instagram and chat apps like WhatsApp Business Platform for a variety of client needs.
According to Infobip’s investigation, SMS and other traditional methods are still useful for sending one-time passwords, two-factor authentication, and time-sensitive messages.
But, users are choosing richer conversational experiences over chat apps when it comes to engagement and assistance.
The data illustrates the continued importance of these channels by showing a 73% and a 51% growth in WhatsApp Business Platform and Email engagements in 2022 compared to 2021.
The number of interactions on Google Business Messages and Apple Messages for Business climbed by 186% and 232%, respectively.
WhatsApp Business Platform, Voice, and mobile app messaging experienced the largest growth in terms of consumer interaction in 2022.
Customers today expect immediate, rich, and human-like interactions with brands and businesses, as evidenced by the 191% and 92% growth in voice and mobile app messaging, respectively.
There is a shift toward conversational everything in numerous industries:
Rich messaging is gaining momentum, with significant gains seen on Google Business Messages, Instagram, and Telegram, reflecting the transition from traditional banking to conversational banking.
MMS, Instagram, and mobile app messaging engagements in the retail and eCommerce sector significantly increased in 2022.
According to the data, the transport and logistics industry last year saw explosive development among rich communications platforms including Instagram, Telegram, and Messenger.
2022 has seen an increase in the use of rich communication on MMS, Messenger, and Google Business Messages by marketing and advertising firms.
For the telecoms sector, MMS and Google Business Messages are the top rich channels for consumer communication.
Link: https://socialbarrel.com/conversational-everything-for-customer-communications-is-growing/140257/
KANBAN TOOL LAUNCHES ITS AI ASSISTANT TO MAKE PROJECT MANAGEMENT SMARTER AND MORE EFFICIENT
Hope Tribune
Kanban Tool Launches Groundbreaking AI Assistant Feature for Visual Project Management on Kanban Boards.
Kanban Tool’s AI Assistant for Kanban Boards introduces a game-changing approach by generating tailored suggestions for workflows, card types, checklists, and tasks.
By using machine learning algorithms, the AI Assistant can analyze user needs and suggest optimal workflows and checklists for each project, allowing users to work smarter, not harder.
Link: https://news.hopetribune.com/story/383236/kanban-tool-launches-its-ai-assistant-to-make-project-management-smarter-and-more-efficient.html
Vectra Unifies AI-driven Behavior-based Detection and Signature-based Detection in a Single Solution
Cision PR Web
Vectra AI, the leader in AI-driven hybrid cloud threat detection and response, announced the introduction of Vectra Match.
Vectra Match brings intrusion detection signature context to Vectra Network Detection and Response (NDR), enabling security teams to accelerate their evolution to AI-driven threat detection and response without sacrificing investments already made in signatures.
With the addition of Vectra Match, Vectra NDR addresses core GRC and SOC use cases enabling more efficient and effective:
Correlation and validation of threat signals for accuracy.
Compliance for network-based CVE detection with compensating controls.
Threat hunting, investigation and incident response processes.
Link: https://cioinfluence.com/cloud/vectra-unifies-ai-driven-behavior-based-detection-and-signature-based-detection-in-a-single-solution/
Ubisoft Introduces Its Own AI That Writes Future Dialogues
Kryzt Bates
Gaming Deputy
In a recent video, the company proudly announces that an in-house AI called ghostwriter be in development.
This should be able to write dialogues for NPCs.
This is intended to help authors, because the AI should thus provide a basis on which Ubisoft’s employees should then build.
The technology is apparently intended to be used primarily for background characters who talk to each other and make noises while players roam the world.
The AI should allow Ubisoft employees to better understand the important cutscenes and focus on general story writing.
Link: https://www.gamingdeputy.com/ubisoft-introduces-its-own-ai-that-writes-future-dialogues-2/
WHY MANY BUSINESSES ARE FAILING AT MACHINE LEARNING
Ryan Offman
Ritz Herald
Businesses Do Not Know What to Do with Machine Learning
They Do Not Hire the Right People
Inadequate Understanding of the Problems Machine Learning is Supposed to Solve
Link: https://ritzherald.com/why-many-businesses-are-failing-at-machine-learning/