Category Archives: Ai

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Watch now!

Customer service is Nordstroms bedrock, and that wasnt going to change just because the retail giant was engaging with customers online and not in the store, Edmond Mesrobian, Nordstroms chief technology officer and chief information officer, said during VentureBeats Transform 2021 virtual conference event today. In conversation with Hari Sivaraman, head of AI content strategy at VentureBeat, Mesrobian shared how Nordstrom revamped its data infrastructure in ways designed to enhance customer experience.

When customers are in the store environment, it is easy to have a compelling personalized experience, thanks to the attentions of a stylist or a salesperson. It is more of a challenge to connect digitally when the online presence tends to be more of a cold and distant experience. This difference forced Nordstrom to take steps to find out how to become closer with customers.

We had to figure out how to get analytical data to make predictions that help us make additions, so that we can actually offer the most compelling experience to our customers, whether online or in-store, and thats the journey we embarked on, Mesrobian said.

Nordstrom Analytical Platform (NAP), the analytics platform that Nordstrom employs, is a real-time, event streamingcentric analytical platform that provides insights on everything from customer services to credit. Reflecting on the start of this project, Mesrobian said that the most important thing is to remember that presenting the information is not about reporting data, but about collecting events that can be translated into actions.

Nordstrom starts by getting the business events created, organized, and streamed in real time. Then the multi-layered analytical models translate the events into predictions that ultimately lead to customer benefits and services. NAP employs open source technology and cloud computing, stitching existing components together to create an analytical platform that drives machine learning in a robust way.

Its off-the-shelf commodity components with a lot of special sources, which essentially translate our business events into a semantically rich object, something that could then be actioned through a model, Mesrobian said.

Before adding the sources, however, Mesrobian needed to make sure that the data was clean and readable to use. That is, the business events had to be completely and accurately represented to start with. As a result, when those events arrive at a point of being transformed into high-level objects, they are accurate and searchable.

We want to transfer the responsibility from the data engineers in the past to the application owners to make sure that their business events were pure, curated, and correct to begin with, and [they are] also aligned with their business goals, Mesrobian explained. The shift in responsibility, he said, is key to the platform architecture process.

With more than 100 AI models leveraged daily at Nordstrom, an end user will indirectly see the benefit from the timely delivery of information. While users have control over their privacy and permission settings, Nordstrom collects user preference information and employs it to provide better selection, better dynamic looks, better style boards, better choices, and so on.

Through AI, the company presented a more robust way of driving discovery and personalization. Nordstrom also launched a fashion map effort to take a natural language-based approach with deep learning so that the model gets to know customers better through conversation, as opposed to taxonomy-driven keyword searches.

In terms of the various AI technologies that Nordstrom uses in building NAP, Mesrobian listed examples such as linear classifier algorithms for computer vision. Those algorithms generate attribution vectors about the product based on pictures, and those vectors are used to drive dynamic loops. The company also uses computer vision algorithms that take shots of customer closets and give them insights about whats there. Tree-based models are also used to detect abnormal signals and fraud. Name it, and we are probably using it, he said.

When building an analytical platform, Mesrobian pointed out that it is important to keep in mind that data is not the crucial aspect, since the end result is to present predictions and leverage them in real-time events. Building from that broad recognition, the three takeaways from the platform include:

When talking about data privacy, Mesrobian said that developers need to be clear about customer proposition and about what information is being collected. We are very mindful of those regulations to make sure that [the customer] is always aware of what we are collecting, and that you have access to that information, he said. In the end, the data needs to create accessible value for the customer, he concluded.

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Nordstrom CTO on AI-powered analytics and consumer insights - VentureBeat

Xero has released Analytics Plus, a new suite of planning and forecasting tools.

Powered by artificial intelligence, Analytics Plus combines cash flow forecasting featuring advanced predictions and business reporting tools, directly available in Xero.

The offering comes at a time when business appetites for strong data insights are renewed, following the difficult pandemic year.

The suite was developed in collaboration through testing and conversation with the broader Xero community during 2020.

Xero has also released Analytics, a free tool for all Xero Business Edition subscribers that combines the existing short-term cash flow tool, which visually projects cash flow over 30 days, and business snapshot tool, providing up to date insights on their business performance. Since their initial release in pilot last year, the cash flow tool has been redesigned with a new look and the ability to view future scheduled invoices and bills, while the business snapshot report can now be viewed on a cash or accrual basis.

To truly grow and thrive, every business needs to have access to trusted, insightful data that helps them understand where they are now, make decisions for today and where they might be headed in the future, said Xero chief product officer Anna Curzon in a statement. Over the past two years, weve worked closely with small businesses and advisors all around the world to build simple, beautiful and powerful tools and reports that give them access to that data, all on the Xero platform. As our business scale has grown, we now are able to provide powerful AI-powered products that use rich, trusted data, to help small businesses not only understand their current position, but make decisions with confidence based on their future potential.

Some features of Xero Analytics Plus include:

Xero Analytics Plus will be included and available in Established/Premium plans. More information on pricing can be found here.

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Xero introduces AI-powered analytics tool - Accounting Today

NVIDIA is gearing up to bring artificial intelligence to the 5G wireless edge starting next year. The company at the Mobile World Congress (MWC) event revealed its plans to make available network cards based on Arm processors that will have the compute power required to run AI inference engines on edge computing platforms. At the core of that effort is a NVIDIA Aerial software development kit (SDK) that NVIDIA is making available to developers.

The NVIDIA Aerial A100 AI-on-5G computing platform will be based on 16 Arm Cortex-A78 processors into the NVIDIA BlueField-3 A100 network card scheduled to become available in the first half of 2022. Those network cards are based on a data processing unit (DPU) that NVIDIA is bringing to market to offload network, storage, security, and now AI workloads from servers.

NVIDIA is in the middle of trying to acquire Arm as part of an effort that would create a behemoth large enough to counter Intel. In addition to running AI on graphical processor units (GPUs), NVIDIA is betting organizations will find it more cost efficient to offload AI inference engines on to Arm processors deployed in a variety of edge computing environments.

Also read: NVIDIA Extends Scope of AI Software Ambitions

Most AI models are trained in the cloud. However, when it comes time to deploy an application infused with AI an inference engine is required. The closer that inference engine runs to the point where data is being collected the better the overall application experience becomes, notes Gilad Shainer, vice president of marketing for NVIDIA. This is where the DPU shines, he said.

Offloading tasks from servers is hardly a new idea. NVIDIA is taking the concept a step further by weaving together graphics processor units (GPUs), traditional CPUs, and DPUs together under a common software architecture. Ultimately, the goal is to create a framework for training AI models using GPUs that then spawn inference engines optimized for processors that also happen to be from NVIDIA and its allies. In most cases, AI models are trained in cloud, but NVIDIA has also been making the case for certified GPU systems that can be deployed in on-premises IT environments.

One way or another the amount of compute horsepower available at the network edge for running AI models is about to substantially increase. The challenge now is optimizing best machine learning operations (MLOps) practices to reduce the friction that many organizations experience when building and deploying AI models today. In most cases, AI models are built by data scientists. Aligning their efforts with application development teams to make sure AI models are ready when applications need to be deployed has proven challenging. In fact, its not clear to what degree MLOps represents a truly separate IT discipline or is simply an aberration that will ultimately be folded into existing IT operations.

One way or another, however, AI is coming to the network edge. The next issue is deciding how best to deliver, manage, secure and update it once it gets there.

Read next: Open Source Platforms Vie with IT Vendors for Management of MLOps

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NVIDIA Aims to Bring AI to the 5G Edge - IT Business Edge

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Watch now!

At VentureBeats third annual Women in AI Breakfast at Transform 2021, leaders in AI and machine learning across industries came together to discuss some of the most urgent questions in the tech sector today, including what responsible AI and engineering means, and the roles and responsibilities of corporates, academia, governments, and society as a whole in getting more diverse voices into the tech sector, and more.

This year the breakfast, presented by Capital One, was moderated by Noelle Silver, the founder of Women in AI. The breakfast opened with a talk about what inclusive engineering means for the tech sector and why it cant just be the responsibility of those who are underrepresented.

Diversity must encapsulate diversity of race and ethnicity and religion, but also economic diversity, diversity of experience, and diversity of education, said Teuta Mercado, director of the responsible AI program at Capital One. And inclusive engineering means ensuring that all voices are represented. Many corporations are starting to do the outreach necessary to bring more diversity into this field with recruiting efforts and imperatives, she noted.

At Capital One, inclusive engineering means ensuring that our products and our services really reflect our customer base, and are accessible to all, said Mercado. Banking customers represent all facets of society, and everyone needs credit services, so its really important that we have diverse teams that are working on and building AI and machine learning so that we can build for all our customers, and not just for some.

Kathy Baxter, principal architect of ethical AI practice at Salesforce, has a background in psychology and human factors engineering, noting that shes benefited from the strong mix of genders seen in psychology and user experience research.

So, when moving into AI, Ive been able to leverage those different experiences of working across roles, she said. Seeking out people with a large variety of expertise and disciplines to contribute and participate in our discussions of what it means to create fair and equitable AI and identifying unintended consequences of AI systems.

Tiffany Deng, who leads the program management team for Responsible AI at Google, explained that her time in the U.S. Army as an intelligence officer prepared her for what she does today: bringing different voices to the table in order to find solutions.

That is what inclusive engineering is about its about giving voice to the underrepresented, and ensuring not only that their voice is heard, but its acted upon, Dang said. And then thinking about going into communities and understanding how a problem may present itself differently in that subtle context, as opposed to what we already think or what weve already seen.

At the World Economic Forum, we think about inclusive engineering globally, said Kay Firsh Butterfield, head of AI and machine learning, and executive committee member of the World Economic Forum.

Companies need to have multi-stakeholder teams so when theyre thinking about policy, that should include academics, nonprofits, governments, and businesses in the room but thats not enough, she said.

Weve been doing some work with a number of the big tech companies to ask what does responsible use of technology actually look like, she said. One of the things we know is that you need diverse product teams. We should all be at the table, with different backgrounds, so that our products use good, non-biased AI.

When talking about increasing the diversity of teams, it comes back to the pipeline issue, or encouraging people of marginalized backgrounds or underrepresented minorities to participate, Baxter added.

I dont want us putting all of the onus on women and other minorities to have to push their way into the door, and I feel like thats what often comes of these discussions, she said. Instead we need to look within our own companies, when we are hiring. Really taking significant time and effort to ensure we have a diversity of sources.

That means not hiring the very first person who meets the job criteria, but continuing to talk to a wide range of people who might be interested in these jobs which takes time and effort, but needs to be done. And then, once individuals are in the companies, its making sure that the environment is actually inclusive.

Too often, there are the micro aggressions and toxicities that not only impact an individuals ability to participate in these conversations, but also to keep them long-term, so that we lose the contributions that they could have given to the company, Baxter said.

There are so many different perspectives around every single day that weve always tried to tap into, in order to come to a better solution for our users, Deng said. A thing that really keeps me going is this idea of intersectionality.

We all have different facets, and identify in very different ways, and companies need to continue to build upon that in truly inclusive ways for these different communities, to ensure that all voices brought into the fold are heard, she said.

Its not enough just to have the women. We have to have men who are in positions of power, also supporters, who also understand that there is a role in their companies for us and that they actually begin to do much better if they have diversity within their country, their cabinet, or within the C-suite, Firth Butterfield said.

So many have struggled against bias in their field, Mercado said, but many things have kept her going.

One, the network: just surrounding yourself with women and men, people who care about what theyre doing, who are passionate about ethical AI or passionate about just doing whats right for our customers for people who are using the products, she said. And then the biggest thing for me is working for a company that has a culture of empowerment, allowing you to do the right thing.

Diversity and inclusion in AI, the erasure of bias in machine learning algorithms, and inclusive engineering is an issue that directly impacts the bottom line, but its so much bigger than that, said Deng.

Me being a Black woman, I think about my children and how can I make the world better for them, and how can I make the world safer for them, she said. AI is omnipresent; its a part of how we go to school, how we district, how things and services are distributed in our communities. And so I want to make that better. This is really an inflection point, and a really great opportunity for us to have deep impact, in all those different types in places in society.

Dont miss the full discussion, from how bias has impacted womens careers in male-dominated fields to how men often miscalculate what women can bring to the table, to how everyone must continue pushing traditional boundaries of these sectors, building truly inclusive community and ensuring that all perspectives are considered at every moment of the design process, and more.

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AI leaders talk intersectionality, microaggressions, and more at Transform Women in AI Breakfast - VentureBeat

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Watch now!

Zoho Corp today unveiled a business intelligence (BI) platform infused with AI that includes data preparation tools that organizations would otherwise have to acquire separately.

The Zoho Business Intelligence (BI) Platform combines Zoho DataPrep with an enhanced version of Zoho Analytics and its existing Zoho Show presentation software and Zoho Sites portal builder tools in a single offering. The goal is to make it simpler for end users to construct and share analytics via visual dashboards that let them launch queries against data using either graphical tools or the Zia conversational AI platform.

Additionally, a Zia Insights tool automatically surface trends and relationships between data sets without requiring an end user to even launch a query, said Chandrashekar LSP, senior evangelist for the BI Platform at Zoho. It also can also be employed to model what-if scenarios. Organizations can, alternatively, employ Zoho DataPrep as a standalone tool in conjunction with other analytics applications.

Ultimately, Zoho is trying to provide a suite of integrated BI tools that enable end users to manage everything from how data pipelines are constructed to how narratives are presented. The platform comes with more than 250 data sources to make it simpler to aggregate data.

Organizations can also create analytics applications that can be sold as complementary Zoho Analytics applications via the Zoho Marketplace or a third-party marketplace run by Shopify, ServiceNow, and Atlassian.

Zoho claims more than 50,000 organizations are already using Zoho Analytics, with a 30% growth rate on a year-over-year basis. Zoho says that 10% of that increase represents customers switching from competitive platforms. The company is also forecasting a 45% increase in analytics application revenue now that it has launched the Zoho BI Platform.

Pricing for the Zoho BI Platform is $8 per user, per month, while the on-premises version is $30 per user, per month. The standalone version of Zoho DataPrep starts at $40 per month for two million rows accessed by three users.

Overall, Zoho is moving to reduce the cost of BI to the point where it becomes accessible to a broader range of organizations. Most of those organizations still rely on spreadsheets as their default BI tool simply because most alternatives are too complex and costly to configure, said Chandrashekar LSP. The friction has to be reduced, he said.

As the level of friction is reduced, the overall size of the BI market will continue to increase, he added.

Theres no doubt the reason so many end users continue to rely on spreadsheets is simple inertia. Rather than learning how to master programming tools to drive analytics using spreadsheets, its a lot easier for most users to employ a BI application. The two primary hurdles have been, first, getting data entered in the BI application, and second, justifying the cost of a BI application when spreadsheets are bundled with Microsoft Office 365.

However, its also clear that, as organizations commit to making data-driven business decisions, spreadsheets are not the best tools for conveying an idea. The visualization tools in BI dashboards enable a broader swath of employees to identify trends in a way that can be easily acted upon. In contrast, discerning trends within long columns of spreadsheet data requires more specialized skill sets that require a significant amount of training to acquire.

Its not clear to what degree Zoho BI Platform might force rivals to similarly reduce the total cost of BI, but as the size of the total potential addressable market grows, the pressure to respond will become greater.

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Zoho launches business intelligence platform infused with AI - VentureBeat

Holly Herndon has released a new artificial intelligence tool which the composer is also referring to as her digital twin called Holly+ that allows users to upload any polyphonic audio and receive a new version of that music sung in Herndons own voice.

Herndon has been working extensively at the intersection of music and AI technology over the past several years (she previously earned a Ph.D. at Stanfords Center for Computer Research in Music and Acoustics). In 2019, she released an album, Proto, which she made with the help of a neural network she developed named Spawn. While Herndon grew Spawn by exposing it to her own voice, the neural network ultimately created its own sounds instead of mimicking Herndons voice like Holly+.

Herndon released and developed Holly+ in collaboration with Never Before Heard Sounds. Its as much a technological and artistic experiment, as it is a response to, and embrace of, the rise of deepfake technology. In a blog post, Herndon noted the spread of celebrity vocal deep fakes on YouTube, and predicted that generating convincing spoken and sung voices will soon become standard practice for artists and other creatives.

Vocal deepfakes are here to stay, Herndon added in a statement. A balance needs to be found between protecting artists and encouraging people to experiment with a new and exciting technology. That is why we are running this experiment in communal voice ownership. The voice is inherently communal, learned through mimesis and language, and interpreted through individuals. In stepping in front of a complicated issue, we think we have found a way to allow people to perform through my voice, reduce confusion by establishing official approval, and invite everyone to benefit from the proceeds generated from its use.

To tackle the complicated of issue of rights ownership, Herndon has established a Decentralized Autonomous Organization for Holly+. The Holly+ DAO will comprise a select group who received unique tokens from Herndon, with tokens essentially representing voting shares (Herndon plans to give the tokens to collectors of my art, friends and family of the project, and other artists). The members of the Holly+ DAO will vote on things like approving official usage of Holly+ and new tool creation; profits made will be split among DAO members and also go toward funding future projects and other expenses.

The vocal deepfake tool is the first of several projects Herndon and Never Before Heard Sounds have in development. Further tools are in development to allow for spoken and sung phrases, and image generation, to be released later this year, Herndon said.

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Holly Herndon Releases AI Deepfake Tool That Lets Others Make Music With Her Voice - Rolling Stone

As psychedelic research returns to the mainstream of medical science, several lesser known drugs are being seen as possible therapies for mental illness. One of these is DMT, which is the subject of a slew of new studies about its potential use in treating depression.

DMT is the common term for N,N-dimethyltryptamine, a powerful psychedelic drug. Its chemical structure is similar to that of serotonin and melatonin, and it is believed to bind to certain serotonin receptors in the brain.

Since the 1960s, scientists have thought that some mammals may produce DMT in their bodies. Its presence in the brains of rodents has been reported, and trace amounts have been found in the human body and cerebrospinal fluid. Exactly what naturally occurring DMT is doing remains a subject of investigation.

Because DMT is also found in a variety of plants, teas containing the drug have been consumed by many native peoples of South America for at least 1000 years, often for religious purposes. It only received serious scientific attention as a therapeutic drug beginning in the 1950s.

Dr. Stephen Szra, a Hungarian psychologist working in the mid-20th century, was denied access to LSD by the Western companies that then produced it. At the time, LSD was considered a powerful tool with applications in spycraft, so neither communist countries nor their biomedical scientists were allowed access to it. Unable to work with that psychedelic, he turned to DMT as an alternative.

Eventually moving to the U.S., he continued to work with DMT and explored its possible applications as well as those of other psychedelics.

Unlike the more famous LSD or psilocybin, DMT trips are often quite short, sometimes lasting as little as five minutes. The short duration is more than made up in its intensity, however, with users reporting extremely vivid hallucinations. Typical doses lead to visions of complex, multicolored geometric patterns, ego death, and altered thought patterns.

DMT has another unique feature: high doses of it can lead to an occurrence called a "breakthrough," at which point the user no longer perceives themself as being in the same plane of existence. The new location can be truly phantasmagoric, ranging from hyperspace to non-Euclidean realms. These strange places are often populated by even stranger creatures known as "machine elves."

The machine elves, named by the ethnobotanist Terence McKenna who popularized DMT in certain circles, have been reported by users since Dr. Szra's experiments. Reports of the elves can vary dramatically, especially in appearance, but users tend to agree that the hallucinated creatures are intelligent and benevolent. The frequency with which these beings are reported may explain the use of DMT as a religious tool for contacting the spirit world. Though some users have speculated if these beings are real, author and psychedelic authority James Kent stresses that they are hallucinations.

Generally speaking, DMT is not legal. The Convention on Psychotropic Substances, an international treaty signed in 1971, bans the drug but not the plants that contain it. Many countries have their own bans on the substance or the plants from which it can be extracted. However, many jurisdictions have exemptions for the use of DMT-containing products (like ayahuasca) by certain religious groups as part of their rituals. Some American cities have recently decriminalized the drug.

Typically, DMT cannot be consumed orally. It must be smoked or, as is common in medical studies, injected. If it is combined with a monoamine oxidase inhibitor, it can be consumed in tea. Variations of this tea, some including different hallucinogenic substances, are often known as ayahuasca from one of the names given it by indigenous South Americans.

While the production and consumption of ayahuasca go back millennia, it has only recently become popular with Western psychonauts. An entire industry of ayahuasca tourism has formed in South America, focused around northeastern Peru, with more than a few psychedelic tourists and celebrities shelling out for a chance to drink the tea in a ritual setting.

By all accounts, a trip on ayahuasca is similar to a DMT trip but with a much longer duration. It also prominently features the purging of the contents of the participants' stomach and bowels. Some practitioners consider this part of the purification process. However, tourists looking for an "authentic" experience may be getting high on hype.

Current studies focus on how the drug works in the brain and how it might be used to treat mental illness. As Dr. Carol Routledge of Small Pharma explained to Freethink, the extreme effects of this drug might be exactly what some patients need, especially when more common drugs like SSRIs have failed:

"A lot of the mental health disorders like depression, like post-traumatic stress disorder, even OCD, have this real negative cycling thought process which leads to ingrained neuronal processes. And it's almost impossible to get out of those, and I think that's why SSRIs don't really even attempt to do that. What psychedelics do is they break that pathway, they break those neuronal connections, and then they increase neuronal connectivity and synaptic connectivity."

Early reports from Small Pharma suggest that DMT, in conjunction with therapy, can be used to help break away from undesired behavioral patterns. Other studies also suggest that it could prove useful in helping with depression and anxiety. Dr. Routledge suggests that the stimulated connectivity among neurons allows the brain to "reset." As a result, these conditions can be more effectively treated. There is also discussion about how the mystical experiences triggered by the drug might help those with mental health problems to examine the root causes.

Whatever the outcome of this research, definitely don't try this at home. At least a dozen tourist deaths have been associated with the consumption of improperly brewed ayahuasca. These poor souls have permanently relocated to a different plane of existence.

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Astronomers train AI to reveal the true shape of galaxies - Big Think

A cyclist rides before the city skyline at Marina Bay in Singapore.

Roslan Rahman | AFP | Getty Images

SINGAPORE Singapore plans to invest $50 million in a program to support research on AI and cybersecurity for future communications structures, Deputy Prime Minister Heng Swee Keat announced on Tuesday.

As part of the Future Communications Research & Development Programme, Singapore plans to set up new communications testbeds in 5G and beyond-5G, support technology development, and build up a local talent pool.

5Grefers to the fifth generation of high-speed mobile internet that aims to provide faster data speeds and more bandwidth to carry growing levels of web traffic. Many new technologies, such as self-driving cars, are underpinned by rapid developments and global deployment of 5G networks. For its part, Singapore plans to have full island-wide standalone 5G coverage by 2025.

The program will "support AI and cybersecurity research for next-generation communications infrastructures," Heng said at the Asia Tech x Singapore conference.

It will "support testbeds for innovative pilots, and provide scholarships for those seeking to pursue research in communications."

Just as globalization drove decades of economic growth around the world, I believe the fast-growing digital economy can propel us to a better future.

Heng Swee Keat

Singapore's deputy prime minister

The program will also aim to build international partnerships and strengthen cross-border collaborations, according to Heng, who is also Singapore's coordinating minister for economic policies.

Singapore will also be launching a digital exchange known as the Singapore Trade Data Exchange, or SGTraDex. It will allow multiple stakeholders such as logistics players, shippers and buyers to share valuable information like real-time cargo locations. The information is said to be encrypted and transmitted, without being stored.

The initiative is expected to stamp out significant inefficiencies around the movement of goods along the supply chain. For example, logistics and shipping companies would be able to optimize cargo handling and operations.

"From the pilots so far, SGTraDex has the potential to unlock more than $150 million of value annually for the supply chain ecosystem," Heng said. It would also speed up the processing of customs clearance, trade financing, insurance and other related activities, he added.

SGTraDex is similar to another initiative launched last year called the Singapore Financial Data Exchange, or SGFinDex. It allows users to sign in with their national digital identity to access their consolidated financial data such as deposits, credit cards, loans and investments from participating banks and relevant government agencies on a single platform.

Heng pointed out that having the ability to allow data to flow securely and seamlessly can help countries unlock the full potential of digitalization.

Southeast Asia's digital economy is fast-growing and its internet sectors are expected to cross $300 billion by 2025, according to an industry report from Google, Temasek Holdings and Bain & Company.

The coronavirus pandemic accelerated the push toward digitalization as many businesses, big and small, had to shift their presence online in the face of social restrictions and lockdowns.

"Just as globalization drove decades of economic growth around the world, I believe the fast-growing digital economy can propel us to a better future," Heng said.

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Singapore is launching a $50 million program to advance research on AI and cybersecurity - CNBC

"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Todays column is by Sarah Rose, SVP International Digital Operations, Data & Platform Ops at IPGs Kinesso.

Artificial intelligence is a heavy and complex topic with tons of deep ethical complications, confusing applications and unknown impacts on many industries. It brings panic to some and sci-fi-infused joy to others.

AI conceptually echoes Ray Kurzweils Singularity. In that scenario, humanity itself will be altered as we become one with AI. With any new technology there is fear and reluctance. In the case of AI, many industries have cleaved to the old ways while lightly playing with the buzzword as a pretense to progress. Our first human instinct is to protect the known, while not going full throttle on integration.

There is no question AI will change industries, markets, company valuations, jobs and our status quo. On the question of legality alone, with little to no federal or state regulation, most industries are perplexed on standard applications and uncertain where to begin.

Even so, the technology has advanced.

Ad tech and mar tech companies now commonly boast of AI-powered optimization tools and bidding methodologies that fuel brand engagement and ROAS. It may not look like a Spielberg film yet, but advertising and marketing technology is starting to integrate the beginnings of technology evolutions that employ self-learning decisioning.

By deconstructing and unpacking artificial intelligence into smaller packets, we can make it more accessible and applicable and provide ourselves with a selection menu on where to start and what to start with.

Types Of AI

There are really three categories of AI technology that can lead us to integrated systems and self-learning tech. The first is Robotic Process Automation (RPA), the second is Machine Learning (ML), and the third is AI (Artificial Intelligence) that is truly self-learning and actualizing.

Robotic process automation (RPA) is built by scripting languages (Python, for example) and is useful in repetitive, simplistic and linear tasks that produce a standard output. This is super basic and widely applied today. For the advertising ecosystem, RPA is great for operational tasks where there are copy-and-paste and server-to-server integrations requiring linear data ingestion. We can find one example in ad trafficking, where APIs between third-party platforms already exist and steps can be standardized. Operationally, this can save time, ensure data accuracy with fewer trafficking errors and conserve resources on quality assurance, campaign management, relationship management and data governance.

Machine learning (ML) is the first step in optimized data science applications, where a human would usually attempt to analyze large data sets to come up to some simple conclusions on patterns. It is difficult for us humans to look at tons of data points in real time and make statistical conclusions that, however minute they be, could be statistically relevant from a Bayesian logic perspective. It is timely and costly to any organization to throw bodies at the problem and find inherent value. However, ML will thankfully set rules for us and look for triggers and flags to meet defined criteria and find value in data. One example is evaluating inventory performance and ROI on long-tail SSP sources and/or to optimize DSP delivery to provide the best ROI in even low-value inventory sources. This is how most AI-powered optimization works and where the bulk of companies are spending data science resources.

When we attain AI, it is a combination of operational RPA and ML technologies. Artificial intelligence in definition is self-learning and making decisions on its own for the benefit of reaching a brands audience and meeting client ROAS deliverables. By integrating RPA, ML and self-learning programming, addressable media plans can shift in real time without human interaction.

AI self-learning technologies have not fully arrived in our industry at scale, but major players have started this journey in simple ways to bring automation (RPA/ML) to the fore. Whether they are startups or well-funded players focused solely on AI applications, companies are beginning to test efficiency gains. Some agencies, publishers and ad technology companies simply license this tech, and it is no surprise that Apple, Google, and Amazon are also innovating advertising practices to set the tone for automation.

While it is not gravity modifying, and we have not reached warp speed, the path has been set. By approaching this journey step by step and knowing what type of tech to integrate at what time and in what way, it becomes less head-spinning.

Let our industry be careful, cognizant, self-aware and available for change to boldly go where no one has gone before.

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AI, No Lie: A Definition And Review Of Marketing Use Cases - AdExchanger

Despite the great promise of AI, we maintain that unless philosophers theorize about and help develop philosophy-specific AI, it is likely that AI will not be as philosophically useful.

In the following guest post*, Caleb Ontiveros, a philosophy graduate student-turned-software engineer and writer, and Graham Clay, a recent philosophy PhD from the University of Notre Dame, discuss the possibility of AI providing a suite of tools that could revolutionize philosophy, and why its important that philosophers help develop and theorize about the role of AI in philosophy.

Philosophy will be radically different in future centuriesperhaps decades. The transformative power of artificial intelligence is coming to philosophy and the only question is whether philosophers will harness it. In fact, we contend that artificial intelligence (AI) tools will have a transformative impact on the field comparable to the advent of writing.

The impact of the written word on philosophy cannot be overstated. Imagine waking up and learning that, due to a freak cosmic accident, all books, journal articles, notebooks, blogs, and the like had vanished or been destroyed. In such a scenario, philosophy would be made seriously worse off. Present philosophers would instantly suffer a severe loss and future philosophers would be impoverished as a consequence.The introduction of writing freed philosophers from being solely dependent on their own memory and oral methods of recollection. It enabled philosophers to interact with other thinkers across time, diminishing the contingent influences of time and space, thereby improving the transmission of ideas. Philosophers could even learn about others approaches to philosophy, which in turn aided them in their own methodology.

It is our position that AI will provide a suite of tools that can play a similar role for philosophy. It is likely that this will require that philosophers help develop and theorize about it.

What is AI? There are, roughly, two kinds of AI: machine reasoning and machine learning systems. Machine reasoning systems are composed of knowledge bases of sentences, inference rules, and operations on them. Machine learning systems work by ingesting a large amount of data and learning to make accurate predictions from it. GPT-3 is an example of thissee discussion here. One can think of the first kind of systemmachine reasoning AIas a deductive and symbolic reasoner and the secondmachine learning AIas learning and implementing statistical rules about the relationships between entities like words.

Like many other technologies, these sorts of systems are expected to continue to progress over the coming decades and are likely to be exceptionally powerful by the end of the century. We have seen exceptional progress so far, the cost of computing power continues to fall, and numerous experts have relatively short timelines. How fast the progress will be is clouded in uncertainty technological forecasting is a non-trivial affair. But it is not controversial that there will likely be significant progress soon.

This being the case, it will be technologically possible to create a number of AI tools that would each transform philosophy:

It is unlikely that these systems will replace human philosophers any time soon, but philosophers who effectively use these tools would significantly increase the quality and import of their work.

One can envision Systematizing systems that encode the ideas expressed by the Stanford Encyclopedia of Philosophy into propositions and the arguments they compose. This would enable philosophers to see connections between various positions and arguments, thereby reducing the siloing that has become more common in the field in recent years. Similar tools could parse and formalize journal papers from the 20th century that are seldom engaged with mining them for lost insights relevant to current concerns. Simulation tools would generate new insights, as when one asks of the tool What would Hume think about the Newcomb problem? Imagine a tool like GPT-3 but one that is better at constructing logical arguments and engaging in discussions. Relatedly, one can envisage a Reasoning system that encodes the knowledge of the philosophical community as a kind of super agent that others can interact with, extend, and learn from, like Alexa on steroids.

Despite the great promise of AI, we maintain that unless philosophers theorize about and help develop philosophy-specific AI, it is likely that AI will not be as philosophically useful.

Let us make this concrete with a specific philosophical tool: Systematizing. A Systematizing tool would encode philosophical propositions and relations between them, such as support, conditional likelihood, or entailment. Philosophers may need to work with computer scientists to formulate the propositions and score the relations that the Systematizing tool generates, as well as learn how to use the system in a way that produces the most philosophically valuable relations. It is likely that Systematizing tools and software will be designed with commercial purposes in mind and so they will not immediately port over to philosophical use cases.

Extending this, we can imagine a human-AI hybrid version of this tool that takes submissions in a manner that journals do, but instead of submitting a paper, philosophers would submit its core content: perhaps a set of propositions, the relations between them, and the relevant logic or set of inference rules. The ideal construction of this tool clearly needs to be thought through. If it were done well, it could power a revolutionary Reasoning system.

However the AI revolution turns out, philosophical inquiry will be radically different in the future. But the details and epistemic values can be shaped by contributions now. Were happy to see some of this work, and we hope to see more in the future.

Excerpt from:

Shaping the AI Revolution In Philosophy (guest post) - Daily Nous