Monthly Archives: March 2017

Forbes

How AI Can Prove Workers' Best Defense In The Race Against Automation Forbes United Technology's announcement last November that its Carrier Corp. plant would keep jobs in Indiana rather than move them to Mexico was heralded as a significant victory for American workers. However, the true impact of the deal was hidden below the ...

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How AI Can Prove Workers' Best Defense In The Race Against Automation - Forbes

From voice translation to self-driving automobile, AI's impact in everyday life will become more and more apparent this year. The AI and deep learning market will experience even more rapid technological advancement, very rapid growth and adoption, and increasing competition for both hardware and software platforms. While AI fears will remain, the public will become more cognisant and comfortable with social media AI applications.

Deep learning training lends itself to what we call "High Density Processing". High density processing applies when algorithms are computationally intensive, having higher ratios of compute operations per byte of memory bandwidth.

In such cases dense clusters of multicore CPUs hosting accelerator technology can provide highly favourable cost-performance and performance per watt. GPUs, because of their ability to provide high density processing, have enabled deep learning computations and have dominated recently.

In 2017, we will start seeing a move from the near monopoly of GPUs for training to hosting on a rather wide variety of multicore and accelerator technologies. These will include Knights Landing/Knights Mill chips and AI accelerators implemented as FPGAs or ASICs. But the GPU will still be widely used.

Short fixed-point arithmetic can offer order of magnitude performance advantages over floating point (see here). These low-power solutions with special purpose architectures can demonstrate better price/performance than even GPUs.

Cloudy options abound with Amazon, Baidu and Microsoft having augmented their GPU-based cloud offerings with FPGA options for AI applications, and Google "supercharging" their Cloud AI with ASICs known as Tensor Processing Units employing short arithmetic.

Intel will also be a leader in bringing accelerators to market, and the combination of Knights Landing plus the Nervana Engine technology to be unveiled later this year looks particularly intriguing.

So in 2017, GPU dominance will be eroded. We think it is premature to talk about a "post-GPU" era, and expect GPUs to maintain a very comfortable lead, but we do expect a much richer mix of technologies to emerge.

Deep learning isn't just about the hardware; software libraries that enable algorithms that take advantage of said hardware and that put the technology into more hands are maybe even more important. We're seeing several libraries battle for dominance in the AI arena. Google's TensorFlow has leapt to the forefront on GitHub and Intel recently responded with their BigDL deep learning framework for Spark. Theano, Microsoft's CNTK and many others the vast majority of which have CUDA support will compete eagerly for developer mindshare. It's too early to call the race, but our prediction is that Microsoft and Intel are the most likely companies to give Google a run for the money.

What's in it for Microsoft is promotion of their software ecosystem, especially around Big Data and IoT. Intel wants increased hardware sales, not surprisingly. And Google appears to be most interested in growing their developer ecosystem to gather new applications that they can then monetise in areas such as self-driving automobiles.

Voice translation will be one of the biggest breakout application segments. International travellers will begin to use it regularly on their mobile phones for short conversations, including ordering food and coffee, buying train tickets, and other shopping.

Text translation in messaging apps has become routine, especially for certain language pairs, facilitating communication between lovers, family and friends, and international project team members.

The many "Lost In Translation" occurrences lead to abundant laughter, frustration and misunderstandings, and even breakups. Despite the limitations of machine translation, the appeal will be irresistible. Usage in personal social interaction will initially be much greater than for business. Could this be the killer app for consumer AI?

AI fears in some respects will ease as the public becomes more cognisant and comfortable with AI applications that are accessed from, or that support applications running on, their mobile-based social media platforms (Google, Facebook, and Twitter in particular).

But concerns around governments' electronic monitoring of social media content and face recognition in public spaces will remain. Facebook, Twitter and others will struggle with the appropriate level of tuning of AI solutions to filter out fake news, offensive videos, and hate speech. Their complicity with nondemocratic government requests for censorship will grow at the expense of freedom of expression. In addition, concerns around middle-class job losses to automated machine learning systems will continue to grow as the globalisation backlash continues.

A raft of AI applications in healthcare including for diagnosis, patient monitoring, and even clinical trials will make steady progress, but there will be no major breakthroughs.

Patient interest will grow significantly as AI healthcare case studies become more numerous and positive outcomes recorded. AI will be seen in a very positive light for medical imaging evaluation and diagnosis, and will begin to lead to significant cost savings. Trial usage for laboratory tests (blood, urine) will grow, but lag usage for imaging applications significantly.

Although patients might be concerned about robots replacing doctors, it will be enhancement, not replacement that is relevant. Generally the patient will not know when doctors and nurses are using AI to support healthcare decisions.

As one example, IBM's Watson technology reached the same diagnosis as oncologists in 99 per cent of cancer cases examined, yet it was also able to explore a wider range of options, since it can extremely rapidly explore the medical literature. Second opinions will be arrived at in realtime, which will save everyone time and money.

Self-driving automobile ("auto-automobile") technology will advance, but will experience speed bumps and citizen backlash as a growing number of trials leads to more accidents, including fatalities, even as statistics point to a significant reduction in accidents. Local and national government restrictions will tighten, and trials will increasingly be focused on lower-risk driving scenarios.

In the high-risk arena, military interest in self-driving ground-based vehicles will become very evident, due to the potential savings of lives and money, and the prospects for using such vehicles to confuse the enemy since soldier casualties will be removed from the equation.

So, to sum up: You're not going to exclusively use GPUs as your AI engine forever, and you're going to have a wide range of choices when it comes to AI libraries. You'll be using AI language translators for pick-up lines on your next international business trip, making you more comfortable with AI applications, but you'll still be afraid of what the government might do with the same technology, and that an AI might take your job.

You'll be healthier because AI medical care applications will speedily diagnose and recommend treatments for your injuries and ills. And, finally, there's a slightly better chance you'll need this enhanced medical care since self-driving cars will be tested in greater numbers. Phew.

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What 2017 holds for AI: Will you fear or embrace our machine overlords? - The Register

Jessica Pothering

Jessica is a business and finance writer, focusing on impact investing, social entrepreneurship and economic development. She previously reported for financial publications covering the global private equity, real estate and insurance markets.

AI see, AI do.

Among the risks in using data-driven AI in low-resource or at-risk communities is that the algorithms will magnify systemic biases.

Care must be taken to prevent AI systems from reproducing discriminatory behavior, such as machine learning that identifies people through illegal racial indicators, write researchers from the Stanford One Hundred Study on Artificial Intelligence.

This week, ImpactAlpha is extracting nuggets from Stanfords century-long effort to understand AIs long-term possibilities and dangers. Theres already an update to yesterdays #2030 segment on self-driving cars: Ford recently announced a $1 billion investment in software for autonomous fleets.

The researchers found AI could be a money-saving lifeline for budget-strapped local and state governments.

Illinois Department of Human Services, for example, is using predictive data modelling to improve prenatal care to high-risk pregnant women.

Cincinnati is using AI to identify and inspect properties that arent up to code.

AI also has potential for developing low-cost community health campaigns, which are otherwise difficult to target and expensive to implement.

This post originally appeared in ImpactAlphas daily newsletter.Get TheBrief.

Photo credit: Scienceofsingularity.com

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Underserved communities: Leveraging AI to improve health and social services in low-resource areas - ImpactAlpha (registration)

Atul Kumar, Chief Product Officer at Mintigo suggests marketers what they can achieve realistically using AI in 2017

2016 was a tremendous year for MarTech. According to Forrester Tech Radar for B2B Marketing Technology and SD16 (SiriusDecisions conference 2016), two of the hottest trends last year were ABM (Account-Based Marketing) and Predictive Marketing. Im sure you all are now busy with deploying your ABM initiatives. Many of you have embraced or thinking of Predictive Marketing.

AI is the red hot topic that is being discussed at c-level in all organizations. We all are beneficiaries of AI in our daily lives; from Alexa and Siri to TacoBot, we are reaping the rewards of AI. Googles AI (AlphaGo) beat the world master in the game GO. In the later half of 2016, Salesforce.com announced Salesforce Einstein while Oracle promised to do better with applications built with Adaptive Intelligence. And self-driving cars are coming soon! Its clear that AI powered business and consumer applications have arrived and you need to be ready to have an intelligent conversation with your boss and peers.

Im sure youve heard of many different terms such as AI, predictive analytics, machine learning, neural networks, deep learning etc. all used interchangeably by the industry. AI is an umbrella term, a branch of computer science whereas machine learning, deep learning etc. are some of the methods and systems of enabling AI. For example, the virtual assistants or Bots, excellent examples of AI, actually use NLP/G (natural language processing/generation) to understand and respond to human requests. There is no need to panic when you hear different terms or some vendor try to tell you that we do AI and others dont! Whats more important that you understand what AI can do for you.

Here is what you can achieve realistically using AI in 2017:

1. Account and lead selection - AI can help you select your best accounts and leads for your inbound, outbound and ABM initiatives. AI platforms such as the one my company (Mintigo) offers, help you to build predictive models for any business scenario, from cross/up-sell to new product launches. You can then explore your total available market using discovery tools offered by these platforms. You can also get new lead names (look-alikes) for your campaigns as needed based on predictive insights.

2. Personalized 1:1 Nurtures - One of the key outputs of predictive analytics is a set of attributes that represents the model. These attributes, often referred to as ideal customer DNA or profile, defines the why or the reasoning of a predictive score. Why John Smith @IBM is more likely to buy your product or services as compared to Andy Cheng @HP. By comparing these sets of attributes for the two, you can easily understand the reasoning. Some of the AI platforms have the ability to provide you detailed attributes into your MAP & CRM systems in real-time. This allows you to nurture an inbound inquiry or send an outbound message in a very personalized manner; for example, if your ideal customer DNA includes modern marketers who spend more than average on digital marketing and use one of the marketing automation systems, you can engage your prospects by sending them offers and messages that are relevant to their needs. AI eliminates the generic messages, and engage your prospects with the right (and relevant) message at the right time.

3. Automated Campaigns - This is an advanced application where an AI application automatically builds the customer journey to accelerate time to sales. This is accomplished by engaging the right prospects with the right offers using the right channels at the right time. Simply put, it is the martech nirvana that only AI can enable. A good percentage of MAP users still use a single step email nurture (nice way to say batch and blast!). Lack of adoption of multi-step nurture can be attributed to lack of resources and time it takes to create one. Those who do run multi-step nurtures, are doing so with limited data and utilizing the art of marketing. The Automated AI driven campaigns solve this issue. However, to take advantage of this you need to organize your content and track each LP uniquely (channel, offer, offer type) using utm codes or equivalent. The target and response data must be available or use a system that offers test and learn abilities.

4. Sales Engagement - Helping sales to intelligently engage with the precious leads and accounts is crucial for the success of your business. AI applications are changing the conventional B2B sales in many different ways; automated conversational Bots (such as Conversica) and other task management bots are good early examples. Other AI applications, such as the one from Mintigo (Mintigos Predictive Sales Coach), are enabling sales to engage their prospects intelligently. These applications use artificial intelligence (AI) to identify the Who, What, Why, and How? of the sales process. To be more specific, Who will buy from your company, What will they buy from you, Why they need it, and How you should engage them in a meaningful conversation. Selling is a tough business and require tremendous amount of research despite enormous efforts put in enabling sales. Many enterprises hire fresh grads to dial for the dollars; the churn could be high if they were not continuously enabled. Marketing creates content that is often buried behind corporate firewalls in content stores. What is needed is always-on intelligence and messaging/content that enables sales to easily find the right prospect and then quickly understand his/her needs and has messages and content at their fingertips to have a meaningful conversation. AI applications are here to help you not only build trust relationship with your counterparts in sales but impact revenue directly.

This list is by no means complete or even close. Numerous AI driven applications are being born everyday. This provides a starting point that will help you navigate the world of AI for marketing. And finally, dont forget to watch ex machina to explore the art of possibility.

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AI for B2B Marketers What to Expect in 2017? - MarTech Advisor