Monthly Archives: July 2021

Article by Darktrace.

Todays attacks still require several humans behind the keyboard making guesses about the sorts of methods that will be most effective in their target network and its the human element that often allows defenders to neutralise attacks.

Even attackers resources are finite. If they find a way any way to scale up their attacks, they will do it. Adversaries think like enterprises: How can I make my hackers more efficient? How can we attack even more targets? How can I achieve more results with fewer resources?

AI has already achieved breakthroughs in multiple fields, including cybersecurity, autonomous vehicles, healthcare, voice assistance and many others. It only makes sense that attackers will turn to AI to reap the same benefits: to understand context, scale up operations, make attribution & detection harder and increase their profitability.

We can expect Offensive AI to be used throughout the attack lifecycle. This can involve several different use cases, including:

Offensive AI will make detecting attacks more difficult. For example, an agent using some form of decision-making engine for lateral movement might not even require command & control (C2) traffic to move laterally.

Eliminating the need for C2 traffic drastically reduces the detection surface of existing malware. In addition, open-source research and projects can also be leveraged to augment every phase of the attack lifecycle that exists today.

This means that the speed, scale, and contextualisation of attacks is expected to grow dramatically. Traditional security controls are already struggling to detect attacks that have never been seen before in the wild be it malware without known signatures, new C2 domains or individualised spear-phishing emails. There is no chance that traditional tools will cope with future attacks as this becomes the norm.

Autonomous Response is necessary not only because humans cannot keep up with todays threat climate, but because these attacks are approaching.

Hundreds of organisations are already using Autonomous Response to thwart machine-speed attacks, new strains of ransomware, insider threats, previously unknown techniques, tools and procedures and many other threats.

IT allows human responders to take stock and strategise from behind the front line. A new age in cyber-defence is dawning, and the effect of AI on this battleground has already proved to be fundamental.

More:

Cybersecurity in the age of AI - SecurityBrief Asia

Roostify: 2020 Google Cloud Technology Partner of the Year Award for AI and ML for document intelligence capabilities.

"We are proud to be recognized by Google Cloud for the work we're doing to improve home lending," says Chris Boyle, Roostify President of Home Lending. "Our partnership has helped us take great strides in our efforts to streamline the home lending process with AI, and take big steps towards making home lending a great experience for both lenders and customers."

"We're proud to recognize Roostify as our Technology Partner of the Year for AI and machine learning," said Kevin Ichhpurani, Corporate Vice President, Global Partner Ecosystem at Google Cloud. "This award recognizes Roostify's commitment to customer success, and its delivery of innovative and impactful solutions on Google Cloud in AI and machine learning. We look forward to building together with Roostify and creating business value for customers with cloud technologies."

The operational efficiencies enabled by Roositfy's machine learning and optical character recognition (OCR) system offer lenders an opportunity to directly address one of the most time-consuming, and expensive, aspects of the loan production process - the underwriting phase. Costs incurred through this phase have become an increasing burden on lenders and customers, something Roostify's AI efforts intend to solve.

Since the beginning of 2020, Roostify has captured more than 10 million documents through its Digital Lending Platform, with monthly averages continuing to rise. AI and machine learning will continue to be a key component of Roostify's ongoing efforts to meet the growing demand for innovative products and services that can enact meaningful improvements on lenders' operations.

About Roostify

Roostify is a home lending technology provider that enables differentiated solutions for mortgage lenders seeking a simpler home lending experience. Unlike one-size-fits-all platforms, Roostify configures its modular technology platform to meet each of our clients' needs and goals. For more information, please visit http://www.roostify.com.

SOURCE Roostify

https://www.roostify.com

Read the original post:

Roostify Named 2020 Google Cloud Technology Partner of the Year for AI and Machine Learning - PRNewswire

MAXREFDES178# camera cube executes low latency AI vision and hearing inferences on a coin cell power budget with reduced cost and size

SAN JOSE, Calif., July 20, 2021 /PRNewswire/ -- Today Maxim Integrated Products, Inc. (NASDAQ: MXIM) unveiled the MAXREFDES178# camera cube reference design, which demonstrates how Artificial intelligence (AI) applications previously limited to machines with large power and cost budgets can be embedded in space-constrained, battery-powered edge devices. The MAXREFDES178# enables ultra-low-power internet of things (IoT) devices to implement hearing and vision and showcases the MAX78000 low-power microcontroller with neural network accelerator for audio and video inferences. The system also contains the MAX32666 ultra-low power Bluetooth microcontroller and two MAX9867 audio CODECs. The entire system is delivered in an ultra-compact form factor to show how AI applications such as facial identification and keyword recognition can be embedded in low-power, cost-sensitive applications such as wearables and IoT devices.

MAXREFDES178# camera cube reference design demonstrates how AI applications previously limited to machines with large power and cost budgets can be embedded in space-constrained, battery-powered edge devices.

AI applications require intensive computations, usually performed in the cloud or in expensive, power-hungry processors that can only fit in applications with big power budgets such as self-driving cars. But the MAXREFDES178# camera cube demonstrates how AI can live on a low-power budget, enabling applications that are time- and safety-critical to operate on even the smallest of batteries. The MAX78000's AI accelerator slashes the power of AI inferences up to 1,000x for vision and hearing applications, as compared to other embedded solutions. The AI inferences running on the MAXREFDES178# also show dramatic latency improvements, running more than 100x faster than on an embedded microcontroller.

Story continues

The compact form factor of the camera cube at 1.6in x 1.7in x 1.5in (41mm x 44mm x 39mm) shows that AI can be implemented in wearables and other space-constrained IoT applications. The MAX78000 solution itself is up to 50 percent smaller than the next-smallest GPU-based processor and does not require other components like memories or complex power supplies to implement cost-effective AI inferences.

Key Advantages

Ultra-Low Power: Reduces power budget for AI imaging applications up to 1,000x

Lower Cost and Size: Cuts the cost of AI with integrated components, reduced board space and compact size

Low Latency: Reduces latency of AI inferences by over 100x compared to other embedded solutions

Commentary

"The next big opportunity in AI is providing machine learning insights at the edge," said Alan Descoins, CTO at Tryolabs. "The MAXREFDES178# shows how Maxim Integrated's AI solution is a breakthrough in power, latency and size that can unlock the possibilities for AI in battery-powered designs."

"Machine learning promises a lot: that machines can make sense of what they see and hear like humans, as well as make more autonomous decisions. Until the MAX78000, the embedded world was left behind because you couldn't implement AI at the edge in a power, cost and size constrained manner," said Kris Ardis, executive director of the Micros, Security and Software Business Unit at Maxim Integrated. "Now the MAXREFDES178# demonstrates how meaningful and powerful AI inferences can be run at the edge, on even the smallest and most energy-conscious devices."

Availability and Pricing

The MAXREFDES178# is available at Maxim Integrated's website for $149 (1000-up, FOB USA); also available from authorized distributors

The MAX78000 is available at Maxim Integrated's website for $8.50 (1000-up, FOB USA); also available from authorized distributors

The MAX32666GWPBT+T is available at Maxim Integrated's website for $6.85 (1000-up, FOB USA); also available from authorized distributors

The MAX9867EWV+T is available at Maxim Integrated's website for $1.00 (1000-up, FOB USA); also available from authorized distributors

All trademarks are the property of their respective owners.

About Maxim Integrated

Maxim Integrated, an engineer's engineering company, exists to solve the designer's toughest problems in order to empower design innovation. Our broad portfolio of high-performance semiconductors, combined with world-class tools and support, delivers essential analog solutions including efficient power, precision measurement, reliable connectivity and robust protection along with intelligent processing. Designers in application areas such as automotive, communications, consumer, data center, healthcare, industrial and IoT trust Maxim to help them quickly develop smaller, smarter and more secure designs. Learn more at https://www.maximintegrated.com.

Logo for Maxim Integrated Products, Inc. (PRNewsfoto/Maxim Integrated)

Cision

View original content to download multimedia:https://www.prnewswire.com/news-releases/maxim-integrateds-hand-held-camera-cube-reference-design-enables-artificial-intelligence-at-the-edge-for-vision-and-hearing-applications-301336982.html

SOURCE Maxim Integrated Products, Inc.

Originally posted here:

Maxim Integrated's Hand-Held Camera Cube Reference Design Enables Artificial Intelligence at the Edge for Vision and Hearing Applications - Yahoo...

Microsoft has released the Human-AI eXperience (HAX) Toolkit, a set of practical tools to help teams strategically create and responsibly implement best practices when creating artificial intelligence technologies that interact with people.

The toolkit comes as AI-infused products and services, such as virtual assistants, route planners, autocomplete, recommendations and reminders, are becoming increasingly popular and useful for many people. But these applications have the potential to do things that arent helpful, like misunderstand a voice command or misinterpret an image. In some cases, AI systems can demonstrate disruptive behaviors or even cause harm.

Such negative outcomes are one reason AI developers have pushed for responsible AI guidance. Supporting responsible practices has traditionally focused on improving algorithms and models, but there is a critical need to also make responsible AI resources accessible to the practitioners who design the applications people use. The HAX Toolkit provides practical tools that translate human-AI interaction knowledge into actionable guidance.

Human-centeredness is really all about ensuring that what we build and how we build it begins and ends with people in mind, said Saleema Amershi, senior principal research manager at Microsoft Research. We started the HAX Toolkit to help AI creators take this approach when building AI technologies.

The toolkit currently consists of four components designed to assist teams throughout the user design process from planning to testing:

The idea for the HAX Toolkit evolved from the set of 18 guidelines, which are based on more than 20 years of research and initially published in a 2019 CHI paper. As the team began sharing those guidelines, they learned that additional tools could help multidisciplinary teams plan for and implement AI systems that aligned with the principles reflected in the guidelines.

The workbook came about because many teams were not exactly sure how to bring the guidelines into their workflow early enough to have a real impact. It is intended to bring clarity when teams have an idea about a feature or product but have not defined it completely, said Mihaela Vorvoreanu, director of UX Research and Responsible AI (RAI) Education for Microsofts AI Ethics and Effects in Engineering and Research (Aether) Committee, which collaborated with Microsoft Research to create the toolkit.

Most importantly, Vovoreanu said, the workbook should be used by a multidisciplinary team, including data scientists, engineers, product teams, designers and others who will work on a project.

You need to be together to have this conversation, said Vorvoreanu, who along with Amershi leads the HAX Toolkit project. The workbook gives you a common vocabulary for different disciplines to talk to each other so youre able to communicate, collaborate and create things together.

That was certainly true for Priscila Angulo Lopez, a senior data scientist on Microsofts Enterprise and Security Data and Intelligence team, who said, The workbook was the only session where all the disciplines got together to discuss the machine learning model. It gave us a single framework for vocabulary to discuss these problems. It was one of the best uses of our time.

In that session, the team collectively discovered a blind spot they realized that a solution they had in place would not in practice solve the problem it was supposed to solve for the user and were thus able to save a lot of time and resources.

Justin Wagle, a principal data science manager for Modern Life Experiences, piloted the workbook for a feature called Flagged Search in the Family Safety app. He said it helped the team think through ethical and sociotechnical impact.

It helped us all data science, product and design collaborate in a way that abstracted us from all the technicality of implementing machine learning, he said. We can talk about these very technical things but it comes down to what that means for the user.

He said the workbook also helped the team better articulate to a consumer exactly how the system works, as well as discover where the system can go wrong and how to mitigate it. Its now part of the process for every project on his team.

The HAX team set out to differentiate itself from existing human-AI interaction resources that lean toward tutorials. The toolkit gives teams specific guidelines as well as practical tools they can start using now and throughout the development lifecycle.

For example, the guidelines are divided into four groups, based on when they are most relevant to an interaction with an AI system: initially; during interaction; when the AI system gets something wrong and needs to be redirected; and over time.

In the Initially group of guidelines, there are two: 1) Make clear what the system can do and 2) Make clear how well the system can do it.

Julie Stanford, a lecturer in the computer science program at Stanford University and a principal at a design firm, used these two guidelines to clearly communicate with a client, based on data her firm had gathered. It turns out that users of the clients product were expecting the product to learn from its mistakes, something the product was not programmed to do.

In the case of Stanfords client, an introductory blurb might be one way to help users better understand the products capabilities. An introductory blurb is one of several design patterns that can be used to implement Guideline 1. The toolkit has 33 design patterns for eight of the 18 guidelines.

The design patterns provide proven solutions to specific problems, so that people do not have to reinvent the wheel and try to create their own processes, Amershi said.

This is how we generally work. We take a human-centered approach to the tools were creating ourselves. We ask what are people most struggling with? What will unblock people the fastest? What will have the biggest impact?

The HAX Design Library contains the patterns, as well as specific examples of how those patterns have been implemented by others. It can be filtered by guideline, product category and application type.

We are asking people to submit examples and patterns, Vorvoreanu said. Were hoping this design library is going to be a community library where people keep contributing and adding examples.

The final tool in the toolkit, the playbook, helps teams anticipate what might go wrong with an AI system by generating scenarios that are most common, based on a type of design. For example, a couple of the most common errors encountered by an AI-powered search feature that uses speech as its input would be transcription issues or background noise.

It can be difficult to know when it can fail until it encounters a failure situation, Vorvoreanu said. The playbook helps a team proactively and systematically explore what types of failures might occur.

Stanford learned about the guidelines during a talk Amershi gave at Stanford University. She has since incorporated them into her Design for AI course.

I felt the guidelines were so robust and thoughtful that I would make them a cornerstone of the user interface design portion of that class, she said.

In the first part of the course, students look at comparative AI experiences online, then evaluate them based on the guidelines. Later, they use the guidelines to design an AI addition for an existing project. Students prioritize which guidelines are most relevant to their project.

Stanford said the guidelines gave the students a common language and helped them to see issues they may not have noticed within the AI experiences they have every day. And when it came time to grade the students design work, Stanford had a comprehensive, fair way of measuring whether they had met their goals.

Its a really flexible tool both for teaching and practicing design, she said.

The HAX team encourages users to share their feedback on the Contact us page and submit examples to the HAX Design Library, so the HAX community can learn together.

We are hoping this can be a trusted resource where people can go to find tools throughout the end-to-end process, Amershi said. We will continue to update and create new tools as we continue to learn and work in this space.

Explore the tools by visiting the HAX Toolkit website.

To learn more about the HAX Toolkit, join Amershi and Vorvoreanu for a webinar on July 21 at 10 am PT. Register for the webinar.

The rest is here:

New toolkit aims to help teams create responsible human-AI experiences - AI for Business - Microsoft

New AI system takes its inspiration from humans: when a human sees a color from one object, we can easily apply it to any other object by substituting the original color with the new one.

Very few species on Earth, including humans, are gifted with the ability to imagine. We can form mental pictures and visualise things, which may not be present in our field of vision at the moment. We can decompose an imagined object into its attributes or components, like imagining a red apple with a green twig. We also can remove the attribute of red and green colourations from the objects and swap them to picture a green apple with a red twig.

Using our ability to imagine, we can tell fantasy-based stories, take inspiration from existing events, modify them to make new stories, and even paint imaginative pictures of how the future might look. Researchers from the University of South Carolina, United States, have now designed an artificial intelligence (AI) based algorithm, which can visualise the unseen.

The scientists claim that the images of red boats and blue cars can be decomposed and recombined to synthesise novel images of red cars using the AI developed by them. Their research has recently been published as a poster at the International Conference on Learning Representations 2021 and has been accepted for publication.

When our brain imagines something unreal, several neural networks are activated to enable it. While machines can perform several tasks much better than humans today, they still lack this basic characteristic of humans. So ideally, researchers are training the AI to replicate the human ability of imaginationto distil out attributes of colour, shape, texture, pose, position etc., from an object and use it to create new objects with novel characteristics.

"We were inspired by human visual generalisation capabilities to try to simulate human imagination in machines," says the study's lead author Yunhao Ge, a computer science PhD student. AI-based algorithms run through extrapolation. Given a large enough number of samples to process, an AI can generate novel samples from them while preserving required traits and synthesising new ones based on extrapolation. In this case, the AI is essentially trained to produce what we commonly know as deepfakes using a concept of disentanglement.

In recent years, deepfakes are among the most prominent features behind the increasing trend of spreading hoaxes and bogus content through the internet. It is a portmanteau of the words deep learningtraining an AI algorithmand fakes. You might have come across deepfakes in situations where you see a viral video of some celebrity speaking things that seems very unlike them. Upon further check, you might find fact-checks of the same viral video establishing that someone superimposed the spoken content onto the face of the said celebrity.

This process of substituting a persons identity (face) while preserving the original movement of the mouth is essentially disentanglement. Attributes of an image or video are being broken down into its simplest components like shape, colour, movement and are being used to synthesise novel content using computers.

In this study, the AI was provided with a group of sample images. It sequestered one image and mined for similarities and differences among the other images to produce an inventory of the available sample features. This is what the researchers call "controllable disentangled representation learning." Then, it recombines this available information to achieve "controllable novel image synthesis," or in human-like terms - imagination.

"For instance, take the Transformer movie as an example," said Ge, "It can take the shape of Megatron car, the colour and pose of a yellow Bumblebee car, and the background of New York's Times Square. The result will be a Bumblebee-colored Megatron car driving in Times Square, even if this sample was not witnessed during the training session."

This study is proof that no scientific advancement can be categorised as an absolute boon or bane. When placed in the wrong hands, the same technology can be used to produce deepfake images and videos and can spread misinformation and fake news. On the other hand, the researchers of this study have provided a potential use of the same for the greater good.

The application framework used here is compatible with nearly any type of data and opens up a range of possibilities. For example, this AI could be immensely helpful in the field of medicine. Doctors can potentially disentangle a drugs medicinal function from all other factors that are unique to the patient and design targeted drugs by recombining them with suitable factors of other patients.

"Deep learning has already demonstrated unsurpassed performance and promise in many domains, but all too often this has happened through shallow mimicry, and without a deeper understanding of the separate attributes that make each object unique," said Laurent Itti, a professor of computer science at the University of South California and the principal investigator of this study. "This new disentanglement approach, for the first time, truly unleashes a new sense of imagination in A.I. systems, bringing them closer to humans' understanding of the world," he adds.

The study titled Zero-shot Synthesis with Group-Supervised Learning was published in the 2021 International Conference on Learning Representations and can be accessed here.

View post:

Scientists Train AI to Visualise the Unseen, Bringing Them Closer to the Human Understanding of the World | The Weather Channel - Articles from The...

Artificial intelligence is gaining more and more attention. Intelligent self-learning programs disrupt many industries, including eCommerce, manufacturing and production lines, transportation, agriculture, logistics and supply chain, and more. Moreover, such programs automate redundant processes and dont require a high level of creativity, increasing its overall effectiveness.

It is difficult to think of an industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries. Andrew Ng, Founder, and CEO of Landing AI

These disruptive forces have started hitting the HR industry as well. And its not just a trend; the innovations brought by AI are going to stay. Moreover, its anything but a temporary phenomenon. The most recent development in HR technology is AI in recruitment. The changes brought by AI in recruitment will be significant since many recruitment aspects have redundant, time-consuming tasks that can be easily automated.

Apart from that, AI can bring innovative solutions to the new-age emerging problems faced in HR and recruitment, like managing a multi-generational workforce, rising mental health issues, promotion, or inclusive culture.

The entire HR industry will be going under major changes while AI makes their jobs easier, faster, and better.

This article will explore the role of AI in recruitment, its possible use cases, top tools available in the market to automate recruitment processes, potential challenges attached with the adoption of AI, and its overall impact.

According to 52% of talent acquisition leaders, the most challenging part of recruitment is screening and short-listing candidates from a large talent pool. When integrated with applicant tracking software (ATS), an AI screening software can make hiring recommendations by utilizing data like candidates performance, merits, experience, etc.

The AI screening software can learn from existing candidates experience and skillsets and make recommendations accordingly.

Screening resumes still make up for the largest part of a recruiters daily schedule. Implementation of AI for resume screening can free up recruiters time to a great extent enabling them to screen effectively from the shortlisted candidates.

AI chatbots for recruitment work as recruiters assistant where chatbots can collect candidates basic information like education, experience and ask basic screening questions. Based on the inputs, the chatbot can rank the recruiter candidates saving their time and efforts.

Chatbots can also resolve candidates doubts that fall into frequently asked questions and set up interviews with worthy candidates, thereby automating almost 80% of top-of-the-funnel/pre-screening activities.

Sourcing candidates to build the recruitment pipeline is a time-consuming and challenging task. AI for candidate sourcing can extend recruiters reach as artificial intelligence can scan millions of profiles in a matter of seconds from multiple job portals, social media platforms, and more.

Normally, a recruiter takes about 6 secs to scan a single resume, and a single corporate job opening attracts around 250 resumes. Thats almost 3-4 working days worth of just scanning resumes for a single position. AI can automate this part of the process, and recruiters can focus on the hiring processs next levels.

Since AI can go through heaps and heaps of data in seconds, it will optimally use your companys database of past candidates. So, rather than spending a great deal of money on sourcing new candidates, you can utilize the existing candidate pool to find a strong candidate who might be fit for existing job openings.

Around 3.2% of the entire workforce of the USA works remotely. And almost 16% of companies hire remote worlds exclusively.

The numbers will grow from here on out since the pandemic redefined the working culture for many companies.

This means it will get harder for recruiters to hire people since hiring remote workers comes with its own set of challenges. However, using AI-powered pre-assessment tools, video interviews combined with a hint of AI, and more can enable recruiters to hire better and faster.

Recruiters can expand their efforts for diversity hiring by implementing AI-powered solutions. For example, a startup called Gapjumpersdotme uses AI to analyze your current screening, hiring, and promotion processes, blind hiring, and creating inclusive job ads.

Around 56% of candidates believe AI may be less biased than recruiters, and about 49% think that implementing AI might increase their chances of getting hired.

Recruiters might be aware of their conscious biases, but it might be possible to be unaware of subconscious biases. And its becoming more and more important to promote diversity in the workplace since it improves employee productivity, happiness, retention, creativity, and more.

Video interviews can save an ample amount of time for both the parties, candidates and employers. And video interviews with AI in the mix can analyze a candidates expressions, capture and analyze their moods, assess their personality traits, and more.

Big companies like Unilever, Dunkin Donuts, IBM, etc., already use AI in their video interviews and claim that it has increased ethnic and socioeconomic diversity in their hiring.

Your employee value propositions cant follow the doctrine of one fit for all. Every employee has a different set of skills, experience, and more importantly, their needs, goals, and aspirations are unique to them.

Whats valuable to one employee can be useless to the other. For example, an employee living within 2 miles from the office building wont use company vehicles or car services. On the other hand, an employee who lives in the suburbs with a good 25 miles between their house and the office will be grateful to have a company vehicle or car service.

Providing custom-tailored value propositions to your employees can result in improved productivity and a happier workforce.

AI can analyze your employees behavior, personality traits, and more to personalize your companys value proposition to an employee.

Such personalization can help you craft lucrative offers for the high-demand candidates, directly improving your overall conversion rates.

Background check is an essential part of the hiring process. This is the stage where the recruiter verifies the candidates credentials, experience, education, etc. Conventionally, this process is quite tedious and time-consuming.

But with AI-powered tools, this process can be made efficient, private, unbiased, and faster.

Checkr is one such platform that automates the process of background checks with its AI-powered solutions. Their solutions are used by companies like Netflix, Airbnb, Instacart, etc.

Companies like Nike, General Dynamics, Intel, and Wayfair, Hiretual helps your talent sourcing teams source and engage the right applicants for opening job positions. Their tools provide a set of features like AI sourcing, market insights for creating effective hiring strategies, personalized outreach with candidates, diversity & inclusive hiring, and 30+ ATS integrations that fetch the right data in real-time.

Used by companies like McDonalds, MARS, MOL Group, and ExxonMobil, XOR helps attract, engage, and recruit candidates efficiently. They offer features like live chat, virtual career fairs, WhatsApp campaigns, on-demand video interviews, recruiter and HR connect, and more.

XOR modernizes your recruitment processes and allows your HR and recruitment teams to leverage the power of AI, the internet, and social media platforms from one platform.

Used by companies like AirAsia, TATA Communications, HULU, and Twilio, Eightfold uses AI to guide your career site visitors to the right job openings. Further, they provide talent acquisition, talent diversity, talent management, and talent experience solutions to streamline every recruitment process aspect.

The conversion rates drastically increase with Eightfold.ai in the picture because of their predictions.

Humanly is a recruitment tool that majorly focuses on diversity hiring and seamlessly integrates with top ATS vendors. With their AI-powered chatbots, your recruitment team can automate candidates screening and interview scheduling in a DEI (diversity ethnicity inclusivity) friendly manner.

Used by Armoire, Tiny Pulse, Swiss Monkey, Oakland Roots, and BPM, Humanly claims to have saved over 60 hours scheduling and screening per job opening and 95% completion of background checks within 48 hours.

Used by companies like Chick-a-fila, Six Flags, Ocada Group, Agoda, and B&M, MyInterview leverages the power of AI and machine learning to analyze candidates answers for professionalism, reasoning, and more.

Their tools provide candidate insights using deep analytics and empower recruiters to customize their candidates experiences. For example, you can review interviews, shortlist candidates, collaborate with stakeholders more efficiently.

Used by Amazon, Onpartners, CT Assist, Three Pillars, and more, Loxo is a hiring and recruitment automation platform. It is a complete HR CRM that comes equipped with recruitment CRM, a talent intelligence system, and an application tracking system.

Known for their modern features like smart grids and task management, Loxo claims to have an updated database of over 530 million people and over 98% customer satisfaction rate.

Used by the likes of Twitter, Salesforce, Waymo, and Rover, Seekout is an AI-powered talent search engine that lets you search for talent in a way thats comfortable for you. You can use their search engine for direct search, boolean search, and apply power filters or leverage AI matching benefits.

Seekouts AI can shortlist candidates based on the job description added. This list is curated either from its own 500 million talent profiles or from your ATS. Additionally, you can reach out to the candidates with personalized messages and automated outreach sequences.

Other prominent features that they provide are talent analysis, candidate engagement, diversity hiring, and more.

You can also download their chrome extension that provides an added level of ease to your recruitment process.

Roughly 75% to 88% of candidates applying for a particular position are underqualified or not a right fit. And for every job opening, it takes recruiters a good 23 hours to scan through all the applications and resumes.

More importantly, the volume of applications will increase in the near future, considering the recent developments in the unemployment rates because of the pandemic. At the same time, the recruitment and HR teams are predicted to remain the same size.

This only means that theyll have to put in more effort with fewer than the necessary number of the hands-on deck.

Since AI can automate candidates screening and scheduling, it saves quite a lot of time and recruiters resources. And since youll be faster and efficient with your processes, the chances of losing a good candidate to your competitors become quite low.

The talent pool gets bigger and bigger every year unless the job listings are for unconventional roles. Then, the recruiters have to screen the entire talent pool to get to the right batch of candidates, which is tiring and time-consuming.

With AI in screening, recruiters will get a list of shortlisted candidates perfect for the jobs. Then, they will have to screen them further on the impersonal attributes like cultural fit, previous organizational behavior, etc. Needless to say, this drastically improves the quality of the hired candidates.

AI-backed tools and software come equipped with high-level analytics, providing recruiters with insights about every aspect of their process. Using these insights, they can easily optimize their operations and make the most of their time and resources.

AI tools like chatbots used for the pre-screening stage are online 24/7. This means whenever any candidates reach out to apply, ask questions, etc., the chatbots are there to clear their doubts and provide the necessary information regarding the job role and responsibilities, company overview, and more.

AI can provide candidates with detailed support throughout the application process, guiding them whenever they get stuck or solving their queries.

This improves their overall experience with the recruitment process and creates a positive image for your brand. This can translate into getting more referrals from the candidates, making it easier for recruiters to expand their reach without investing time or money.

Chatbot conversations can lack the human touch, can be perceived as robotic, and it is quite possible that the bots might not be able to parse human lingo, cultural context, everyday slang, etc.

Moreover, around 80% of candidates would prefer to have human interactions over AI-powered chats with bots.

The candidates experience might get impacted negatively by the use of AI.

To mimic human intelligence, AI-powered programs require the input of a great deal of data. If not, then you cant expect accuracy in results generated from implementing AI-powered programs and tools.

AI-powered tools are created based on previous data. The data originated from years of recruiters screening of candidates. The chances of recruiters having tapped into their unconscious bias in their screening and hiring candidates are quite high.

So, the possibility of AI learning human biases is not lost on us.

The only way to ensure that your AI tools are not replicating human biases is to have a vendor thats well-aware of such issues and has taken steps to remove patterns of such biases.

Changing old ways, adopting new technologies and practices can be hard, in general. As a result, it is quite possible that HR and recruitment professionals might take these tools and software with a grain of salt.

Learning to work with a new tool and process is as hard as unlearning old ways and processes.

AI can be unreliable in candidate screening, especially when it encounters unconventional resumes in new formats or fonts. For example, when the AI cant recognize the resumes pattern, it might end up rejecting a well-suited candidate for the position.

Its easier forAI to understand data like years of experience, education level, etc. But when it comes to impersonal attributes like cognitive aptitude, personality traits, cultural fit, soft skills, etc. might get lost on AI.

This means you can lose an excellent candidate if your entire requirement process is relying on AI-powered tools.

Nothing is perfect. Everything comes with its own set of pros and cons. AI in recruitment is the same.

On the one hand, you have recruitment not quite willing to adopt the latest technologies or fearing that itll replace them, which is quite true. But on the other hand, this will essentially lead to the increased importance of human work and human touch in the recruitment process.

In the entirety, the pros ofimplementing AI in recruitment outweigh the cons by large. However, lets not forget that its pros are not limited to recruiters; AI positively impacts candidates experience, improves organizations efficiency and cultures, and helps form better teams.

The cost of not keeping up with the technology can be quite high, especially if your industry is saturated with many big players.

The key still remains in finding the right balance between automation and manual work. Keep the processes human requires a high level of creativity, empathy, and analyzing intangible attributes. Automate the ones that are repetitive and time-consuming.

Read the rest here:

The Role of AI in Recruitment (+ Top 7 AI Recruiting Tools) - ReadWrite

AI has set its destructive sights on one of lifes greatest pleasures: visiting galleries.

An Italian museum has started using AI-powered cameras tomeasure the attraction value of works of art.

The ShareArt devices collect visual data on spectators, such as how long they look at a painting and where on thecanvas their attention is focused.

Thanks to simple data elaboration, an observers gaze can be translated into a graphic, Stefano Ferriani, one of the researchers behind the project, told Bloomberg CityLab. We can detect where most of peoples attention is concentrated.

The system could help curators understand which artworks and layouts appeal to visitors. A useful purpose, I suppose but the tech fills me with dread.

Data analytics have influenced art for centuries, from counting footfall at theaters to projecting album sales.

In more recent years, the Relativity Media studio has been using predictive algorithms to select movies to produce.

Im not in this for the art, said Relativity founder Ryan Kavanaugh in 2012.

The company has since filed for bankruptcy twice.

In galleries,AI can help improve accessibility and makeexhibitions more interactive. But its a horribly reductive measurement of artistic value.

Our attention is often drawn to the controversial or bizarre before the subtle and thoughtful. Brilliant works could be overlooked because they dont generate sufficient engagement.

Furthermore, our expressions are, at best, an unreliable measurement of our feelings. We all show our emotions differently and algorithms often fail to discern them particularly when theyre applied to minority groups.

The ShareArt system is currently focused on gaze analysis, but with rules on masks easing, it could soon move on to facial gestures. That sounds like another good reason to wear a face covering even if COVID disappears.

Greetings Humanoids! Did you know we have a newsletter all about AI? You can subscribe to itright here.

Continue reading here:

Galleries are using AI to measure the quality of art SET ME AFLAME - The Next Web

RALEIGH InsightFinder has raised an additional $2,010,000 from 10 investors, including IDEA FUND Partners.

In an SEC Filing, the company noted the round involved an option, warrant, or other right to acquire another security, as well as security to be acquired upon the exercise of option, warrant, or other right to acquire security.

The company was founded by Dr.Helen Gu, a professor at N.C. State, and provides software to clients like Dell, Credit Suisse, and China Mobile that detect system anomalies without thresholds. The firm says that this results in predicting severe incidents hours before they happen, and that the technology can automatically pinpoint root causes.

The company is definitely hiring in the Triangle, said Gu. InsightFinder is under rapid growth and we see increasing demand from customers because almost every business relies on a reliable IT system to function and InsightFinder provides the right product for this pressing market demand.

NCSU professors artificial intelligence startup raises $2M including Valley money

According to a statement from the company, the funding comes fromFellows.Fund, Silicon Valley Future Capital, Eastlink Capital, and Brightway Future Capital. IDEA Fund Partners also participated in the round, said Lister Delgado, managing partner.

We funded the company not too long ago, said Delgado. That prior funding came in January 2021 and was $725,000 from seven investors, according to an SEC filing.

This is an expansionof that funding and was a bit opportunistic, said Delgado about the most recent raise. It is a highly reputable group of investors that we wanted to add to the cap table. We were not really pursuing the money.

Delgado is a member of the companys board of directors.

See the original post:

Flush with funding, AI startup led by NCSU professor is hiring in the Triangle - WRAL Tech Wire

Legal contract management software can drastically streamline contract creation, review, execution and management processes that are often fraught with complications and errors.

Data from the World Commerce & Contracting Association supports this idea. The organization recently surveyed its 70,000+ members about their contract challenges and priorities and found that 85% experience pressure for contract simplification. Another 81% said they have plans to implement contract automation. These points speak to the fact that poorly managed contracts lead to lost revenue, higher costs and more time devoted to manual tasks for all parties involved.

To understand the actual value of legal contract management software, its helpful to recap the inefficiencies associated with contract handling.

Manual processes open the door for errors and slow down overall contract execution. For example, approvals and negotiations done via email are often sluggish or overlooked. Untracked revisions can lead to confusion, conflict or non-compliance and a lack of standard legal language may result in lengthy review times or require lawyers to get involved.

Disparate repositories result in inefficient reporting and reduce contract visibility. Contracts spread out over different repositories, departments and geographical locations make monitoring corporate contracts holistically almost impossible. Without tracking expiring contracts and renewals, companies run the risk of compliance exposure as well as revenue loss.

Changes occur over the lifetime of a contract, including renewal dates, pricing, emerging legal requirements and other events. They require amendments and approvals from the contract parties. If these changes arent managed, implemented and communicated correctly and quickly, organizations can increase compliance risks for themselves and all parties involved.

Legal contract management software can reduce the average hours spent on contracts by 20%, accelerate review and save on costs. It does this by:

CLM centralizes contract storage and automates the request, creation, negotiation, execution and management of any type of contract.

When you combine AI with CLM, you can lower the number of contracts needing to be reviewed. This gives the reviewer the ability to speed up a review and provide consistency across processes. AI also significantly enhances contract management after execution by extracting and obtaining usable data from executed, legacy and third-party paper contracts.

Not all CLM AI is created the same. To get the full benefits of contract lifecycle management solutions, you should carefully evaluate AI for both the pre- and post-signature phases of contract management.

Read the original post:

Getting Started with Legal Contract Management Software and AI - JD Supra