Mediaboss Marketing

For those who choose to let their cats live a more or less free-range life, there are usually two choices. One, you can adopt the role of servant and run for the door whenever the cat wants to get back inside from their latest bird-murdering jaunt. Or two, install a cat door and let them come and go as they please, sometimes with a present for you in their mouth. Heads you win, tails you lose.

Theres another way, though: just let the cat ask to be let back in. Thats the approach that [Tennis Smith] took with this machine-learning kitty doorbell. Its based on a Raspberry Pi 4, which lives inside the house, and a USB microphone thats outside the front door. The Pi uses Tensorflow Lite to classify the sounds it picks up outside, and when one of those sounds fits the model of a cats meow, a message is dispatched to AWS Lambda. From there a text message is sent to alert [Tennis] that the cat is ready to come back in.

Theres a ton of useful information included in the repo for this project, including step-by-step instructions for getting Amazon Web Services working on the Pi. If youre a dog person, fear not: changing from meows to barks is as simple as tweaking a single line of code. And if youd rather not be at the beck and call of a cat but still want to avoid the evidence of a prey event on your carpet, machine learning can help with that too.

[via Toms Hardware]

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Machine Learning Gives Cats One More Way To Control Their Humans - Hackaday

We are officially in the era of the social media content creator.

What was once a hobby is now a bonafide career path. Brands of all sizes and industries want in on the creator economy, creating new functions within social media teams.

Hiring for a role youve never filled before can be tricky, especially when its a role thats still emerging within its discipline. If you want to level up your social strategy with a social media content creator, heres everything you need to know.

A social media content creator is an individual who creates and shares content intended to educate or entertain an audience across social media platforms.

The internet offers several avenues for content creation. You can write blogs, share newsletters, draft web copythe list goes on. This subset of content creators is solely focused on understanding and building their audience on social media.

On the surface, that may seem limiting. In reality, its anything but. Social media trends and functionality change daily. Their focus on the channel gives them an unparalleled understanding of what works on their preferred networks.

While there certainly is overlap between social media content creators and social media managers, theyre not one and the same.

Content creation is one of many responsibilities a social media manager might take on. Social media management also includes:

These efforts make the most out of the channel as a business function. They also take a lot of time. Adding video and graphic production to the list can quickly create an unsustainable workload.

Thats why social media managers and social media content creators are a match made in heaven. Creators allow managers to offload content production, allowing them to focus on more strategic initiatives.

Its pretty common for social media content creators to maintain a presence across most major social media networks.

Why? Its good business.

Each network has its own engagement advantages. Maintaining a presence across a handful of networks ensures that a creator can continue to grow and connect with their audience as trends shift and evolve.

For example, Tyler Gaca (AKA @ghosthoney across all networks) rose to popularity on TikTok but now uses:

This is great news for marketers who are revving up partnerships across several networks at once. Our research found that more than half anticipate using Instagram, Facebook and TikTok for creator collaborations within the next three to six months.

Think of it this way: If a marketer were to partner with a social media content creator that has a major following on TikTok, theyd only reach audiences that are currently using TikTok.

If they partner with a creator that has a following on TikTok, Instagram and Twitter, they can request that a sponsored post be shared across all networks. Thats way more reach with just a little bit of extra effort on behalf of both parties.

If youre interested in hiring a social media content creator, there are a few different ways you can go. You could:

Each of these options has its own pros and cons depending on your needs. Regardless of which one you choose, one thing is certain: these individuals should focus solely on creating content.

That may not seem like enough responsibility to justify an entire role, but a lot of work can go into a single post. Greer Hiltabidle, a TikTok creator for 360i, broke down the role responsibilities in a recent interview with Marketing Brew: Youre a director; youre an actor; youre a filmmaker; youre a writer. You do wardrobe, set design.

On top of all these production duties, social media content creators are also tasked with creative ideation. Adding too many additional responsibilities on top of that can easily overburden a creator.

Theres nothing more intimidating than a blank page, especially when writing a job description.

Thankfully, there are a lot of places you can turn to for inspo. For starters, our pack of social media manager job description templates has a digital content creator role description thats ready to personalize.

You can also look at existing job listings to kickstart your creativity. For example, you may want to mimic how this contract social media content creator role from Blizzard outlines the collaboration and creation expectations.

At the end of the day, as long as youre clear on your purpose and realistic about your expectations, youll find the candidate you need.

Social media content creator salaries are a bit of a wild west. At the end of the day, it all comes down to your chosen compensation structure.

If youre going with a freelance social media content creator, weve gathered some price-per-post baselines in our most recent data report on the creator economy.

These estimates can be influenced by several factors, including brand investment and creator-to-brand affinity. Depending on your industry, comped products or affiliate marketing opportunities could work as a supplement to smaller base pay.

Those looking to hire an in-house content creator ditch the price-per-post payment method in favor of an annual salary. According to Glassdoor, the estimated total pay for a social media content creator in the U.S. is $69,419.

There are tons of ways brands can partner with social media content creators to drive their business forward.

Whether you want to generate more engagement or simply reduce the burden of always-on content creation, theres a creator that can help you meet your social media goals.

The key is to work with creators who know whats trending online. Memes and viral video formats can rise and fall in popularity in less than 24 hours. Partnering with someone who can quickly put their own spin on a social media moment is crucial to maintaining brand relevance.

The more you work with social media content creators, the easier it will be to develop a process thats collaborative and on brand without sacrificing timeliness. As you prepare to take the plunge, heres how you can lay a foundation for powerful, lasting relationships with creators.

You wouldnt buy a car without taking it for a test drive first, right?

Think of working with freelance creators as that initial test drive. It allows you to better understand what you want out of a partnership, which networks yield the best results and what content formats work well with your audience.

If youve never worked with a creator before, try using freelancer apps like Fiverr or Upwork to parse through your options. If you find a creator that you establish a strong working relationship with, theres always an opportunity to expand the contract or offer them a full-time position.

If theres one certainty in social media, its that people can sniff out a fake partnership a mile away.

According to Kerrie Smith, a content strategist for Twitter ArtHouse, authenticity is everything.

Consumers are on the lookout for partnerships that feel forced and many creators now have the luxury of turning down brand opportunities that dont feel like the right fit for their business goals. People are no longer averse to #ad, but they will reject inauthentic advertising. Invest in tools to help you listen to your community on Twitter and uncover the creators that are talking about your brand already. Leverage insights to align with creators that are driving the trending moments that your brand can participate in.

Its no secret brands have struggled to diversify the talent they work with.

Not only is it bad PR, its also bad business sense. Inclusive campaigns bring unique perspectives to your content, broadening your reach beyond any single group of consumers.

For example: When Hagen-Dazs wanted to increase its brand awareness among non-white consumers, they partnered with award-winning actor Lena Waithe and marginalized social media content creators to develop branded content. Representation matters in marketing.

When brands fail to account for the diversity of their target audience, dont be surprised to see members of marginalized communities take their business elsewhere.

As Rachel Karten, social media consultant and author of the Link in Bio newsletter puts it: Its not hard to spot a brand that has an overbearing approval process.

When working with creators, you need to be ready to abandon the multi-step approval process youre used to. Nothing shorts originality like putting a post through a few dozen rounds of editing. Plus, it runs you the risk of missing a trend entirely.

To reap the full benefits of partnering with social media content creators, you need to give them the creative freedom and creator tools needed to do their thing. Remember: youre paying for their perspective and unique voice. Stifling that can hurt both your brand and your relationship with a creator.

Trends and consumer preferences are changing faster than ever before. If youre looking for new ways to maintain your brands relevance online, social media content creators may be the solution for you.

For everything else you need to know to evolve your social media strategy, check out the latest edition of the Sprout Social Index. Inside, youll find data-backed insights on what people want from brands and what other marketers are doing to keep up.

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What is a social media content creator and when should you hire one? - Sprout Social

The last century alone has seen a meteoric increase in the accumulation of data and we are able to store unfathomable quantities of information to help us solve problems known and unknown. At some point the ability to optimally utilise these vast amounts of data will be beyond our reach, but not beyond that of the tools we have made. At the North-West University (NWU), Professor Marelie Davel, director of the research group MUST Deep Learning, and her team are ensuring that our ever-growing data repositories will continue to benefit society.

The teams focus on machine learning and, specifically, deep learning, is creating magic to the untrained eye. Here is why.

Machine learning is a catch-all term for systems that learn in an automated way from their environment. These systems are not programmed with the steps to solve a specific task, but they are programmed to know how to learn from data. In the process, the system uncovers the underlying patterns in the data and comes up with its own steps to solve the specific task, explains Professor Davel.

According to her, machine learning is becoming increasingly important as more and more practical tasks are being solved by machine learning systems: From weather prediction to drug discovery to self-driving cars. Behind the scenes we see that many of the institutions we interact with, like banks, supermarket chains and hospitals, all nowadays incorporate machine learning in aspects of their business. Machine learning makes everyday tools from internet searches to every smartphone photo we take work better.

The NWU and MUST go a step beyond this by doing research on deep learning. This is a field of machine learning that was originally inspired by the idea of artificial neural networks, which were simple models of how neurons were thought to interact in the human brain. This was conceived in the early forties! Modern networks have come a long way since then, with increasingly complex architectures creating large, layered models that are particularly effective at solving human-like tasks, such as processing speech and language, or identifying what is happening in images.

She explains that, although these models are very well utilised, there are still surprisingly many open questions about how they work and when they fail.

We work on some of these open questions, specifically on how the networks perform when they are presented with novel situations that did not form part of their training environment. We are also studying the reasons behind the decisions the networks make. This is important in order to determine whether the steps these models use to solve tasks are indeed fair and unbiased, and sometimes it can help to uncover new knowledge about the world around us. An example is identifying new ways to diagnose and understand a disease.

The uses of this technology are nearly boundless and will continue to grow, and that is why Professor Davel encourages up-and-coming researchers to consider focusing their expertise in this field.

By looking inside these tools, we aim to be better users of the tools as well. We typically apply the tools with industry partners, rather than on our own. Speech processing for call centres, traffic prediction, art authentication, space weather prediction, even airfoil design. We have worked in quite diverse fields, but all applications build on the availability of large, complex data sets that we then carefully model. This is a very fast-moving field internationally. There really is a digital revolution that is sweeping across every industry one can think of, and machine learning is a critical part of it. The combination of practical importance and technical challenge makes this an extremely satisfying field to work in.

She confesses that, while some of the ideas of MUSTs collaborators may sound far-fetched at first, the team has repeatedly found that if the data is there, it is possible to build a tool to use it.

One can envision a future where human tasks such as speech recognition and interaction have been so well mimicked by these machines, that they are indistinguishable from their human counterparts. The famed science fiction writer Arthur C Clarke once remarked that any sufficiently advanced technology is indistinguishable from magic. At the NWU, MUST is doing their part in bringing this magic to life. DM

Author: Bertie Jacobs

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Machine and deep learning are a MUST at the North-West... - Daily Maverick

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do.

But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter a must be added to end of a word to make the masculine form feminine in Serbo-Croatian.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

The researchers trained and tested the model using problems from linguistics textbooks that featured 58 different languages. Each problem had a set of words and corresponding word-form changes. The model was able to come up with a correct set of rules to describe those word-form changes for 60 percent of the problems.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words. It is especially unique because the system discovers models that can be readily understood by humans, and it acquires these models from small amounts of data, such as a few dozen words. And instead of using one massive dataset for a single task, the system utilizes many small datasets, which is closer to how scientists propose hypotheses they look at multiple related datasets and come up with models to explain phenomena across those datasets.

One of the motivations of this work was our desire to study systems that learn models of datasets that is represented in a way that humans can understand. Instead of learning weights, can the model learn expressions or rules? And we wanted to see if we could build this system so it would learn on a whole battery of interrelated datasets, to make the system learn a little bit about how to better model each one, says Kevin Ellis 14, PhD 20, an assistant professor of computer science at Cornell University and lead author of the paper.

Joining Ellis on the paper are MIT faculty members Adam Albright, a professor of linguistics; Armando Solar-Lezama, a professor and associate director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; as well as senior author

Timothy J. ODonnell, assistant professor in the Department of Linguistics at McGill University, and Canada CIFAR AI Chair at the Mila -Quebec Artificial IntelligenceInstitute.

The research is published today in Nature Communications.

Looking at language

In their quest to develop an AI system that could automatically learn a model from multiple related datasets, the researchers chose to explore the interaction of phonology (the study of sound patterns) and morphology (the study of word structure).

Data from linguistics textbooks offered an ideal testbed because many languages share core features, and textbook problems showcase specific linguistic phenomena. Textbook problems can also be solved by college students in a fairly straightforward way, but those students typically have prior knowledge about phonology from past lessons they use to reason about new problems.

Ellis, who earned his PhD at MIT and was jointly advised by Tenenbaum and Solar-Lezama, first learned about morphology and phonology in an MIT class co-taught by ODonnell, who was a postdoc at the time, and Albright.

Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human. We wanted to see if we can emulate the kinds of knowledge and reasoning that humans (linguists) bring to the task, says Albright.

To build a model that could learn a set of rules for assembling words, which is called a grammar, the researchers used a machine-learning technique known as Bayesian Program Learning. With this technique, the model solves a problem by writing a computer program.

In this case, the program is the grammar the model thinks is the most likely explanation of the words and meanings in a linguistics problem. They built the model using Sketch, a popular program synthesizer which was developed at MIT by Solar-Lezama.

But Sketch can take a lot of time to reason about the most likely program. To get around this, the researchers had the model work one piece at a time, writing a small program to explain some data, then writing a larger program that modifies that small program to cover more data, and so on.

They also designed the model so it learns what good programs tend to look like. For instance, it might learn some general rules on simple Russian problems that it would apply to a more complex problem in Polish because the languages are similar. This makes it easier for the model to solve the Polish problem.

Tackling textbook problems

When they tested the model using 70 textbook problems, it was able to find a grammar that matched the entire set of words in the problem in 60 percent of cases, and correctly matched most of the word-form changes in 79 percent of problems.

The researchers also tried pre-programming the model with some knowledge it should have learned if it was taking a linguistics course, and showed that it could solve all problems better.

One challenge of this work was figuring out whether what the model was doing was reasonable. This isnt a situation where there is one number that is the single right answer. There is a range of possible solutions which you might accept as right, close to right, etc., Albright says.

The model often came up with unexpected solutions. In one instance, it discovered the expected answer to a Polish language problem, but also another correct answer that exploited a mistake in the textbook. This shows that the model could debug linguistics analyses, Ellis says.

The researchers also conducted tests that showed the model was able to learn some general templates of phonological rules that could be applied across all problems.

One of the things that was most surprising is that we could learn across languages, but it didnt seem to make a huge difference, says Ellis. That suggests two things. Maybe we need better methods for learning across problems. And maybe, if we cant come up with those methods, this work can help us probe different ideas we have about what knowledge to share across problems.

In the future, the researchers want to use their model to find unexpected solutions to problems in other domains. They could also apply the technique to more situations where higher-level knowledge can be applied across interrelated datasets. For instance, perhaps they could develop a system to infer differential equations from datasets on the motion of different objects, says Ellis.

This work shows that we have some methods which can, to some extent, learn inductive biases. But I dont think weve quite figured out, even for these textbook problems, the inductive bias that lets a linguist accept the plausible grammars and reject the ridiculous ones, he adds.

This work opens up many exciting venues for future research. I am particularly intrigued by the possibility that the approach explored by Ellis and colleagues (Bayesian Program Learning, BPL) might speak to how infants acquire language, says T. Florian Jaeger, a professor of brain and cognitive sciences and computer science at the University of Rochester, who was not an author of this paper. Future work might ask, for example, under what additional induction biases (assumptions about universal grammar) the BPL approach can successfully achieve human-like learning behavior on the type of data infants observe during language acquisition. I think it would be fascinating to see whether inductive biases that are even more abstract than those considered by Ellis and his team such as biases originating in the limits of human information processing (e.g., memory constraints on dependency length or capacity limits in the amount of information that can be processed per time) would be sufficient to induce some of the patterns observed in human languages.

This work was funded, in part, by the Air Force Office of Scientific Research, the Center for Brains, Minds, and Machines, the MIT-IBM Watson AI Lab, the Natural Science and Engineering Research Council of Canada, the Fonds de Recherche du Qubec Socit et Culture, the Canada CIFAR AI Chairs Program, the National Science Foundation (NSF), and an NSF graduate fellowship.

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AI that can learn the patterns of human language - MIT News

Digital customer experience transformation provider recognized for its commitment to delivering innovative, people-first solutions

SAN FRANCISCO, Aug. 30, 2022 /PRNewswire/ -- Hero Digital, a leading customer experience digital transformation company, announced today that it has been selected as a winner in the 2022 MarTech Breakthrough Awards program, taking home the Customer Experience Innovator of the Year Award.

The Customer Experience Innovator of the Year award reinforces Hero Digital as a leader in the industry and commends its dedication to providing best-in-class digital customer experience (CX) solutions. Hero received the award due to its robust CX digital transformation approach, which consists of a holistic review of each client's business and customer opportunity to identify the ideal customer experience strategy based on data and analytics.

Hero Digital's world-class experience design and engineering team leads clients through customer journey mapping, service design, technology selection, enterprise architecture planning and technology implementation.With this recognition, Hero joins the ranks of other past winners such as Adobe, Axciom, and Mailchimp, as a key leader in customer experience.

"Hero's unique, human-focused approach to digital customer experience transformation is rooted in what people value most and supporting companies in leveraging marketing technology that enhances people's lives," said Patrick Frend, President of Hero Digital. "Receiving the Customer Experience Innovator of the Year award is validation of our efforts to further the digital customer journey, and we could not be more honored."

The mission of the Martech Breakthrough Awards program is to recognize excellence and champion the creativity, prowess and success of companies, technologies and products in the fields of Marketing, Sales and Advertising Technology. This award is an additional recognition of Hero Digital's industry leading MarTech consulting, implementation, and performance that has gained recognition from leading MarTech companies including Adobe, Oracle and Optimizely.

This award continues a pattern of momentum for Hero Digital following the recent recognition as Adobe's Emerging Partner of the Year, solidifying Hero Digital as a leader in digital transformation. Hero Digital's one-of-a-kind customer experience approach is trusted by companies such as Airbnb, Comcast, US Bank and Salesforce to help power digital transformation through a cohesive customer-employee ecosystem. For more information about Hero Digital, please visit http://www.herodigital.com.

About Hero Digital

Hero Digital is a leading independent digital customer experience company leveraging strategy, design, technology, marketing, and data, to solve the critical digital transformation needs of the Fortune 1000. Hero Digital's purpose is to bring moments of Truth & Beauty into people's lives by creating customer experiences that are good for people and good for business. Hero Digital's blended teams help Fortune 1000 companies like Comcast, U.S. Bank, Salesforce, Twitter, UnitedHealthcare, and TD Ameritrade Institutional invent, transform, and perform to deliver new business value.

To work with Hero Digital or learn more, visit http://www.herodigital.com.

About MarTech Breakthrough

Part of Tech Breakthrough, a leading market intelligence and recognition platform for global technology innovation and leadership, the MarTech Breakthrough Awards program is devoted to honoring excellence in marketing, ad and sales technology companies, products and people. The MarTech Breakthrough Awards provide a platform for public recognition around the achievements of breakthrough marketing technology companies and products in categories including marketing automation, AdTech, SalesTech, marketing analytics, CRM, content and social marketing, website, SEM, mobile marketing and more. For more information, visitMarTechBreakthrough.com.

Media Contact:Richie Roesner[emailprotected]

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Hero Digital wins "Customer Experience Innovator of the Year" award in 2022 Martech Breakthrough Awards - PR Newswire