Artificial intelligence has been making headlines over the last 12 months in domains like health care, finance, face recognition and more. Marketing, however, doesn’t seem to be getting the same kind of coverage, despite major developments in the application of AI to marketing analytics and business intelligence.
Five or 10 years ago, only the world’s savviest, most heavily funded companies had a serious foothold in artificial intelligence marketing tech. As we enter 2017, there are hundreds of AI marketing companies all over the world (including some that have gone public, like RocketFuel). These companies are making AI and machine learning accessible to large corporations and SMBs (small and medium-sized businesses) alike, opening new opportunities for smarter marketing decisions and approaches.
Over the last three months, we surveyed over 50 machine learning marketing executives (email registration required for the full report data) to get a sense of the important trends and implications of AI over the next five years.
Below, I’ve highlighted three major trends that impact the theme of “Intelligent Content.”
While most of our executives voted “Search” as the AI marketing tool with the highest profit potential today, “Recommendation and Personalization” topped the list for ROI potential in the coming five years.
While search requires users to express their intent in text (or speech), recommendation pulls from myriad points of data and behavior — often bringing a user to a) what they were truly looking for, or b) what the advertiser wanted them to find.
The implications of recommendation in content marketing are numerous. Below I’ll list just a few:
First, recommendation engines help serve the content most likely to engage readers. In the past, this was done with simple text analysis or tools like elastic search. The “recommended” content was better than a random guess, but it was by no means truly optimized for user engagement.
Companies like Boomtrain and Liftigniter are developing technologies to tailor content to individual visitors, displaying material most likely to keep them on the site based on their previous engagements, purchases, clicks and more.
Second, programmatic advertising (like that used on giant platforms like Facebook and Google AdWords) is often used to drive users directly to content before seeing a product page or being asked to book an appointment. Many ad networks (Facebook included) don’t allow for direct lead generation and instead prefer to engage users with the right content first before looking for a conversion.
Ad networks are partial to keeping user experience high in addition to driving engagement on ads, which is a delicate balance. Companies that can leverage these programmatic platforms to target the right prospects with the right content are the most likely to win.
Third, we see entire content marketing platforms at the heart of business models. One such example is Houzz.com, a site that hosts millions of articles and photo albums about home improvement and decoration. This content ecosystem links to and references millions of home goods products (from throw rugs to couches and more), and “recommendation” drives the entire experience.
Houzz is one of the best current examples of “intelligence content” directly tying to sales, and I suspect that in the coming five years, we’ll see elements of their business model become much more prevalent.
Content generation is a complex machine learning problem, and until recently, it’s been relegated to big-budget media firms working in quantitatively oriented domains (namely sports and finance). Yahoo Finance uses natural language generation (NLG) to turn information about stocks and bonds into coherent, human-readable articles, saving time for Yahoo’s writers so that they can complete more important and creative tasks.
NLG is now being used in a vast number of business applications including compliance, insurance and more — and a quick visit to the “solutions” page at Narrative Science shows a plethora of use cases and case studies for machine-written content.
While domains like finance and compliance involve strict, formulaic transformations of cold data into readable text, executives in the field are excited about its profit potential, too. Rather than simply saving costs on human writers, intelligent content generation will alter existing content (and/or create new content) to help driving marketing goals. As Laura Pressman, manager of Automated Insights, explained in our survey:
Content generation has high profit potential in the coming five years. Personalization and segmentation can be achieved through altering the content text to speak to certain groups of people, across different platforms, highlighting unique and targeted features that are most important to each specific segment.
When we polled our batch of executives about the most meaningful applications of artificial intelligence in marketing, we didn’t want to leave out their opinions about which businesses or industries would be most able to take advantage of AI’s advancements in marketing.
“Industry” didn’t seem to have much to do with the predicted success that a company might have with AI marketing tech. Much more important was the way the company did business and sold products. For a business to take advantage of AI, the most important traits (as predicted by our batch of executives) include:
The above three qualities repeated themselves again and again in our survey responses, along with strong predictions that “Digital Media” companies and “E-commerce/Consumer Retail” companies would be most poised to take advantage of AI in marketing. As Lisa Burton, chief data officer of AdMass, explained in the survey:
Advertisers and e-commerce businesses have the highest potential gain from machine learning because of the ease of measurement and quick feedback needed to train and improve machine learning algorithms.
While B2C and retail companies seem to have an edge on “quantifiability” and attribution to sale, some of our respondents also hinted at the strong opportunity in B2B. Leveraging the many content and interaction touch points in a B2B sale will aid greatly in “cracking the code” on B2B marketing attribution, which is undoubtedly valuable.
In the coming five years, it may be possible that attribution and recommendation take off quickly in retail, while adoption in services and B2B sectors will provide more of an “ahead of the curve” advantage in industries where tech adoption is slower.
A Different Kind of Self Service
Artificial intelligence (AI) is the wave of the future, the influential technologist told CNBC, calling it the “singular thing that will be larger than all of human tech revolutions added together, including electricity, [the] industrial revolution, internet, mobile internet — because AI is pervasive.”
“It is the decision engine that will replace people,” Lee said, adding that AI capabilities far exceed those of humanity.
For example, he said, companies in which his firm has invested can accomplish feats such as recognizing 3 million faces at the same time, or dispersing loans in eight seconds.
“These are things that are superhuman, and we think this will be in every industry, will probably replace 50 percent of human jobs, create a huge amount of wealth for mankind and wipe out poverty,” Lee said, later adding that he expected that displacement to occur in the next 10 years.
Lee’s views on technology are widely followed in part because he previously headed Google China before founding his venture capital firm. He has also held executive posts at many other major tech companies, including Apple and Microsoft, and he has celebrity status in the industry and in China — with millions of social media followers on multiple platforms.
Speaking with CNBC from the Global Mobile Internet Conference in Beijing, Lee tackled the big question: Will human still have a place in the world as machines grow more intelligent?
The answer, he said, is that nothing can replace human-to-human interaction.
“Touching one’s heart with your heart is something that machines, I believe, will never be good at,” he said, explaining that service jobs should be considered “first-class” employment.
And while all of this change is happening, traditional companies like banks, insurance firms and hospitals, are simply moving to slow, Lee said. And it’s too bad, he added, because they “possess the biggest treasure” in the form of troves of data.
“Because AI is about taking data into insight and decision, so I anticipate [the] internet sector, entrepreneurial sector, to continue to grow and in many cases displace and even wipe out traditional companies in China,” he said.
Chinese tech tycoons like Tencent and Alibaba , for instance, have forged the way in mobile payments — it’s become pervasive for consumers to pay for everything from rent to transportation through mobile wallets and transfers. Meanwhile, China’s giant state-owned banks are just barely starting to catch on, Lee said.
MIT researchers and associates have come up with a breakthrough method of remotely stimulating regions deep within the brain, replacing the invasive surgery now required for implanting electrodes for Parkinson’s and other brain disorders.
The new method could make deep-brain stimulation for brain disorders less expensive, more accessible to patients, and less risky (avoiding brain hemorrhage and infection).
Working with mice, the researchers applied two high-frequency electrical currents at two slightly different frequencies (E1 and E2 in the diagram below), attaching electrodes (similar those used with an EEG brain machine) to the surface of the skull.
At these higher brain frequencies, the currents have no effect on brain tissues. But where the currents converge deep in the brain, they interfere with one another in such a way that they generate low-frequency current (corresponding to the red envelope in the diagram) inside neurons, thus stimulating neural electrical activity.
The researchers named this method “temporal interference stimulation” (that is, interference between pulses in the two currents at two slightly different times — generating the difference frequency).* For the experimental setup shown in the diagram above, the E1 current was 1kHz (1,000 Hz), which mixed with a 1.04kHz E2 current. That generated a current with a 40Hz “delta f” difference frequency — a frequency that can stimulate neural activity in the brain. (The researchers found no harmful effects in any part of the mouse brain.)
“Traditional deep-brain stimulation requires opening the skull and implanting an electrode, which can have complications,” explains Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT, and the senior author of the study, which appears (open access) in the June 1, 2017 issue of the journal Cell. Also, “only a small number of people can do this kind of neurosurgery.”
If this new method is perfected and clinically tested, neurologists could control the size and location of the exact tissue that receives the electrical stimulation for each patient, by selecting the frequency of the currents and the number and location of the electrodes, according to the researchers.
Neurologists could also steer the location of deep-brain stimulation in real time, without moving the electrodes, by simply altering the currents. In this way, deep targets could be stimulated for conditions such as Parkinson’s, epilepsy, depression, and obsessive-compulsive disorder — without affecting surrounding brain structures.
The researchers are also exploring the possibility of using this method to experimentally treat other brain conditions, such as autism, and for basic science investigations.
Co-author Li-Huei Tsai, director of MIT’s Picower Institute for Learning and Memory, and researchers in her lab tested this technique in mice and found that they could stimulate small regions deep within the brain, including the hippocampus. But they were also able to shift the site of stimulation, allowing them to activate different parts of the motor cortex and prompt the mice to move their limbs, ears, or whiskers.
“We showed that we can very precisely target a brain region to elicit not just neuronal activation but behavioral responses,” says Tsai.
Last year, Tsai showed (open access) that using light to visually induce brain waves of a particular frequency could substantially reduce the beta amyloid plaques seen in Alzheimer’s disease, in the brains of mice. She now plans to explore whether this new type of electrical stimulation could offer a new way to generate the same type of beneficial brain waves.
This new method is also an alternative to other brain-stimulation methods.
Transcranial magnetic stimulation (TMS), which is FDA-approved for treating depression and to study the basic science of cognition, emotion, sensation, and movement, can stimulate deep brain structures but can result in surface regions being strongly stimulated, according to the researchers.
Transcranial ultrasound and expression of heat-sensitive receptors and injection of thermomagnetic nanoparticles have been proposed, “but the unknown mechanism of action … and the need to genetically manipulate the brain, respectively, may limit their immediate use in humans,” the researchers note in the paper.
The MIT researchers collaborated with investigators at Beth Israel Deaconess Medical Center (BIDMC), the IT’IS Foundation, Harvard Medical School, and ETH Zurich.
The research was funded in part by the Wellcome Trust, a National Institutes of Health Director’s Pioneer Award, an NIH Director’s Transformative Research Award, the New York Stem Cell Foundation Robertson Investigator Award, the MIT Center for Brains, Minds, and Machines, Jeremy and Joyce Wertheimer, Google, a National Science Foundation Career Award, the MIT Synthetic Intelligence Project, and Harvard Catalyst: The Harvard Clinical and Translational Science Center.
* Similar to a radio-frequency or audio “beat frequency.”
We report a noninvasive strategy for electrically stimulating neurons at depth. By delivering to the brain multiple electric fields at frequencies too high to recruit neural firing, but which differ by a frequency within the dynamic range of neural firing, we can electrically stimulate neurons throughout a region where interference between the multiple fields results in a prominent electric field envelope modulated at the difference frequency. We validated this temporal interference (TI) concept via modeling and physics experiments, and verified that neurons in the living mouse brain could follow the electric field envelope. We demonstrate the utility of TI stimulation by stimulating neurons in the hippocampus of living mice without recruiting neurons of the overlying cortex. Finally, we show that by altering the currents delivered to a set of immobile electrodes, we can steerably evoke different motor patterns in living mice.
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In a paper published (open access) June 1 in the journal Cell, researchers report that they have cracked the code for facial identity in the primate brain.
“We’ve discovered that this code is extremely simple,” says senior author Doris Tsao, a professor of biology and biological engineering at the California Institute of Technology and senior author. “We can now reconstruct a face that a monkey is seeing by monitoring the electrical activity of only 205 neurons in the monkey’s brain. One can imagine applications in forensics where one could reconstruct the face of a criminal by analyzing a witness’s brain activity.”
The researchers previously identified the six “face patches” — general areas of the primate and human brain that are responsible for identifying faces — all located in the inferior temporal (IT) cortex. They also found that these areas are packed with specific nerve cells that fire action potentials much more strongly when seeing faces than when seeing other objects. They called these neurons “face cells.”
Previously, some experts in the field believed that each face cell (a.k.a. “grandmother cell“) in the brain represents a specific face, but this presented a paradox, says Tsao, who is also a Howard Hughes Medical Institute investigator. “You could potentially recognize 6 billion people, but you don’t have 6 billion face cells in the IT cortex. There had to be some other solution.”
Instead, they found that rather than representing a specific identity, each face cell represents a specific axis within a multidimensional space, which they call the “face space.” These axes can combine in different ways to create every possible face. In other words, there is no “Jennifer Aniston” neuron.
The clinching piece of evidence: the researchers could create a large set of faces that looked extremely different, but which all caused the cell to fire in exactly the same way. “This was completely shocking to us — we had always thought face cells were more complex. But it turns out each face cell is just measuring distance along a single axis of face space, and is blind to other features,” Tsao says.
“The way the brain processes this kind of information doesn’t have to be a black box,” Chang explains. “Although there are many steps of computations between the image we see and the responses of face cells, the code of these face cells turned out to be quite simple once we found the proper axes. This work suggests that other objects could be encoded with similarly simple coordinate systems.”
The research also has artificial intelligence applications. “This could inspire new machine learning algorithms for recognizing faces,” Tsao adds. “In addition, our approach could be used to figure out how units in deep networks encode other things, such as objects and sentences.”
This research was supported by the National Institutes of Health, the Howard Hughes Medical Institute, the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech, and the Swartz Foundation.
* The researchers started by creating a 50-dimensional space that could represent all faces. They assigned 25 dimensions to the shape–such as the distance between eyes or the width of the hairline–and 25 dimensions to nonshape-related appearance features, such as skin tone and texture.
Using macaque monkeys as a model system, the researchers inserted electrodes into the brains that could record individual signals from single face cells within the face patches. They found that each face cell fired in proportion to the projection of a face onto a single axis in the 50-dimensional face space. Knowing these axes, the researchers then developed an algorithm that could decode additional faces from neural responses.
In other words, they could now show the monkey an arbitrary new face, and recreate the face that the monkey was seeing from electrical activity of face cells in the animal’s brain. When placed side by side, the photos that the monkeys were shown and the faces that were recreated using the algorithm were nearly identical. Face cells from only two of the face patches–106 cells in one patch and 99 cells in another–were enough to reconstruct the faces. “People always say a picture is worth a thousand words,” Tsao says. “But I like to say that a picture of a face is worth about 200 neurons.”
Caltech | Researchers decipher the enigma of how faces are encoded
Primates recognize complex objects such as faces with remarkable speed and reliability. Here, we reveal the brain’s code for facial identity. Experiments in macaques demonstrate an extraordinarily simple transformation between faces and responses of cells in face patches. By formatting faces as points in a high-dimensional linear space, we discovered that each face cell’s firing rate is proportional to the projection of an incoming face stimulus onto a single axis in this space, allowing a face cell ensemble to encode the location of any face in the space. Using this code, we could precisely decode faces from neural population responses and predict neural firing rates to faces. Furthermore, this code disavows the long-standing assumption that face cells encode specific facial identities, confirmed by engineering faces with drastically different appearance that elicited identical responses in single face cells. Our work suggests that other objects could be encoded by analogous metric coordinate systems.
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The Technology Driving Transportation Summit is a one-day Executive Summit focused on the development & deployment of the most cutting edge technologies as well as the education of the next generation workforce. Thought leaders from across transportation, artificial intelligence, automation, cyber security, labor, and technology companies turn thoughts and research into action, setting the market conditions for the success of the rapid adoption of new technologies in transportation and laying the ground work for public acceptance of these technologies.
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Laugh, Cry, and Let the Scars of the Past Be Dissolved in Silence
For 21 days, Laugh for 3 hours a day for 7 days, Cry for 3 hours a day for 7 days, and then Sit Silently, for 3 hours a day for 7 days. The course includes OSHO Dynamic Meditation for the 3rd week, and optionally for the first two weeks, and OSHO Kundalini Meditation and the OSHO Evening Meeting every day.
Whenever any experience is not fully lived in the moment, it leaves a residue inside us. It may be a small thing or something really significant but those residues or scars stay in the unconscious blocking our natural ability to flower to our full potential.
Whatever is left in the unconscious remains, waiting for an opportunity to be expressed, accepted, and resolved.
When we allow all our un-laughed laughter, our un-cried tears, and our un-lived silence to be experienced, we can allow whatever is in the unconscious to flow freely and be lived now – and be resolved forever.
The laughter, tears and silence do their work without the need for words, discussion, or analysis as is typical of conventional “therapy.” You just participate with totality and sincerity, and the rest happens by itself.
The OSHO Mystic Rose provides that opportunity.
Details of this 21-day course:
As Osho explains:
For seven days we laugh for no reason at all for three hours each day.
For the second seven days, we cry for no reason at all for three hours a day.
The third part is the time for the “Watcher on the Hill,” just watching whatever is happening inside or out.
As Osho further explains:
Non-verbal process, minimal translation may be required.
A 21-Day Course – ideally experienced as part of the Multiversity Plus
OSHO Mystic Rose & Facilitating
Get paid to get in the best shape of your life training for
only 7 minutes a day 3 times per week!!
Joel Therien had a successful occupation as a personal instructor as well as he began his 2nd expert career right from college as a Lung and Cardiac Recovery Professional at the Montford Healthcare facility in Ottawa, Ontario, Canada.Joel Therien had a growing job as an individual instructor and also he started his 2nd specialist profession right from the university as a Lung and also Heart Rehabilitation Expert at the Montford Health center in Ottawa, Ontario, Canada.
Although he had an amazing employer, magnum opus associates as well as a respectable income of $52,000 a year, Joel wanted much more. He intended to supply a better future for himself as well as his family. So … the idea of doing tension screening at a health center for the next 40 years started to shed its charm. For this reason, he quit after eight months into it.
However, at 27 years of age, he started to shed his health and wellness as well as ended up being a “clinical mystery”, as he described his illness. He had actually been misdiagnosed with either Lou Gehrig’s disease, multiple sclerosis, or brain cancer cells. He went from a lean muscular 240 pounds with 7% body fat to 165 pounds.
As a body builder, this deeply worried him and this is when he turned to the internet to seek responses.
Joel made use of the net to locate the solution or factor for his ill-health. He claimed that it was “ASPARTAME”, (additive code E 951), the artificial sweetener discovered in all diet plan sodas and also in lots of foods today.
You see, he was on diet regimen of different items which contained aspartame, to preserve his low body fat degrees. And also he felt that because of the over intake of aspartame, he was poisoning himself.
Although aspartame has been authorized by numerous globe food authorities, the argument on its toxicity still goes on.
Joel Therien spent almost 12 hours a day for two full years online in a tiny 800 sq. ft. row house with his young household. Although his expenditures were fairly little he was near emotional, physical, as well as financial ruin.
” I still wasn’t really feeling well physically and I still had actually not made a solitary sale online in 2 years. The psychological stress was overwhelming.” Despite the health setbacks, he had the ability to develop an extremely successful multi million buck service that has actually been up and running currently considering that 1998.
That firm is International Digital Opportunities (GVO) Joel credit histories the long-term 18-year success of GVO by providing real service to service products that entrepreneur and also network marketers in fact require!! At a wonderful rate!
His adage “You do NOT have a great possibility if you do NOT have great items that individuals want to pay for in spite of the opportunity”.
Joel admits, during the climb of GVO he was still suffering from the results of aspartame poisoning. Constant food allergic reactions, crippling migraine headaches were the standard. However he pressed on.
However, after a great deal of homeopathy, acupuncture, and organic foods, Joel claims his worst days are behind him. “For the past 5 years, I have been once more in the health club routinely. It feels wonderful to have constructed the exact same body I as soon as had in my 20ies now also better at age 44″.
” You understand,” states Joel, “Getting so ill was a true blessing in camouflage, I would have never established GVO and also now with 18 years experience as well as sensation healthy again I have the ability to introduce NowLifstyle.com Our very first Health and also Wellness way of life platform that is also our initial really customer based product.”.
NowLifeStyle.com will certainly be without a doubt the greatest and most effective program we have actually ever before done. Since I am healthy and balanced again, I am able to share my true passions for health and also health with the world … You incorporate that with 18 years of internet marketing and also MLM experience and also 33 years of Individual Training experience as well as you have a real home run!!!
So yeah … I do not mind obtaining a little bit sick, it deserved the wait, it is time to change millions of lives around the globe.
The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it’s being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to explore how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.
Deep Learning and AI – An Overview
This is the epoch of artificial intelligence (AI), when the technology came into its own for the mainstream enterprise. AI-based tools are pouring into the marketplace, and many well-known names have committed to adding AI solutions to their product mix—General Electric is pushing its AI business called Predix, IBM runs ads featuring its Watson technology talking with Bob Dylan, and CRM giant Salesforce released an AI addition to their products, a system called Einstein that provides insights into what sales leads to follow and what products to make next.
These moves represent years of collective development effort and billions of dollars in terms of investment. There are big pushes for AI in manufacturing, transportation, consumer finance, precision agriculture, healthcare & medicine, and many other industries including the public sector.
AI is becoming important as an enabling technology, and as a result the U.S. federal government recently issued a policy statement, “Preparing for the Future of AI” from the “Subcommittee on Machine Learning and Artificial Intelligence,” to provide technical and policy advice on topics related to AI.
Perhaps the biggest question surrounding this new-found momentum is “Why now?” The answer centers on both the opportunity that AI represents as well as the reality of how many companies are afraid to miss out on potential benefit. Two key drivers of AI progress today are: scale of data, and (ii) scale of computation. It was only recently that technologists have figured out how to scale computation to build deep learning algorithms that can take effective advantage of voluminous amounts of data.
One of the big reasons why AI is on its upward trajectory is the rise of relatively inexpensive compute resources. Machine learning techniques like artificial neural networks were widely used in the 1980s and early 1990s, but for various reasons their popularity diminished in the late 1990s. More recently, neural networks have had a major resurgence. A central factor for why their popularity waned is because a neural network is a computationally expensive algorithm. Today, computers have become fast enough to run large scale neural networks. Since 2006, advanced neural networks have been used to realize methods referred to as Deep Learning. Now, with the adoption of GPUs (the graphics processing unit originally designed 10 years ago for gaming), neural network developers can now run deep learning with compute power required to bring AI to life quickly. Cloud and GPUs are merging as well, with AWS, Azure and Google now offering GPU access in the cloud.
There are many flavors of AI: neural networks, long short-term memories (LSTM), Bayesian belief networks, etc. Neural networks for AI are currently split between two distinct workloads, training and inference. Commonly, training takes much more compute performance and uses more power, and inference (formerly known as scoring) is the opposite. Generally speaking, leading edge training compute is dominated by NVIDIA GPUs, whereas legacy training compute (before the use of GPUs) by traditional CPUs. Inference compute is divided across the Intel CPU, Xilinx/Altera FPGA, NVIDIA GPU, ASICs like Google TPU and even DSPs.
Deep Learning and AI – An Overview
The Difference between AI, Machine Learning and Deep Learning
The Intersection of AI and HPC
Are AI/Machine Learning/Deep Learning in Your Company’s Future?
The Difference between AI, Machine Learning and Deep Learning
Accelerating Analytics for the Enterprise
Are AI/Machine Learning/Deep Learning in Your Company’s Future?
The Intersection of AI and HPC
Bringing Artificial Intelligence to Life
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