AI has some very common roadblocks: 1. Most data scientists are expected to be their own product owners. Meaning, data scientists - who are programmers and mathematicians by training - are expected to become students of macroeconomics, supply and demand, marketing, customer qualification, pain points and value propositions, market definition, and the many other nuances of product strategy. This usually happens because most companies don't have a discipline of placing a product strategist/owner/manager as the head of the AI efforts. Product management has very well-defined frameworks for building web-based/mobile apps (SaaS apps). But very little has been done to articulate how to design a good algorithm, how to define metrics and dimensions and ML objectives so that a data scientist can hit the ground running, armed with clarity. Hence, most AI initiatives in non-AI companies fall flat on their faces. 2. AI doesn't make intuitive sense to statisticians, or people with a basic understanding of math, so there is a big resistance to some of its messaging, which can come across as oversimplification. For instance, whereas in traditional business-applied stats you can't just add more data in (it has to be cleaned and preprocessed), machine learning allows you to infuse messy, half-complete data and still keep improving the algorithms. I have seen many projects get halted by those in power - who have a vested interest in maintaining an old-school approach to regression modeling and predictions that is vastly outpaced by today's ML/AI capabilities. I'm sure there are more examples, but hopefully this helps.

AI is a broad category. But the first distinction to be familiar with is Elon Musk's AI vs. Google's AI. In other words: the AI of the movies is very different from the AI being developed in leading companies today. In theory, it may be someday possible to make an actually "artificial intelligence" - a mind that can reproduce itself and has all the creative, linguistic and generative functions of the human mind. But no one is anywhere close to building this today. This is very important to understand, because AI is not as unapproachable or intimidating as it initially seems. Machine Learning - which is what any company does that claims to do "AI" - is all about automated pattern recognition. AI/ML tools are ones that have been trained to recognize specific patterns in massive amounts of data. Take, for example, face recognition. The first step - even before recognizing WHO is in a picture - is to recognize that there is a face in the picture. If there is no face, no recognition. And if there is some frog's head that looks like a face, the algorithm may get tripped up trying to match it to a person's face. If it indeed selects Donald Trump or Hillary Clinton as the most probably match for this frog face, you have a PR crisis on your hands. This is how it works: offer thousands, millions of images tagged as either 'no-face' or 'face' - a '0' or a '1' - a 'null' or a 'match'. The null images have no human face present. The match images have at least one face present. Then run it through an image segmentation or object identification training algorithm to create a face detection algorithm. Then test it on images that were not part of the training set (these not-used-before images are called the 'holdout set') and evaluate the performance of the algorithm. If needed, train again and again. You'll notice a couple things here: 1. You can make an algorithm for almost anything. Yes! You could make an algorithm to recognize the Iron Throne in Game of Thrones. There are many obscure algorithms out there for very specific purposes. Some brands I've talked to have requested algorithms for identifying a certain shoe in a massive image database, for instance. 2. There are many versions of the same algorithm. Yes - many person, face, car, horse, chair, airplane, etc. etc. etc. algorithms exist, all with varying degrees of accuracy. See this link (if Clarity will let you click it) http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6 3. It's all about probability. Yes. Ultimately the output of every ML algorithm is a probability of how likely a thing is to be the thing you want it to be. In other words, how close to 1.0 (certainty) a prediction is for whether a face is present. Now, empowered with an accurate face detection algorithm, you can take the next step: face recognition. This will follow the same steps, except now you are creating training sets where 'null' means there is a human face that is NOT the target face (the person you want to recognize) and 'match' means there human face present is the target face. If you want to find Donald Trump's face in a massive library of images, you'd first train an algorithm on a large array of images, some of which have Donald Trump's face. You'd want the 'null' images to be as diverse and vast in their coverage of non-Donald Trump faces as possible. Preferably, there'd be some very Donald-like faces in there, marked as not-Donald. Notice here we have a layering of one algorithm on top of another: first, run the face detection. Then, run the face recognition. My current gig involves over 30 sequenced and parallelized (they run at the same time, separately) algorithms fo image and audio classification. Not all AI is about images though. Some AI is for chatbots, which just means it's making a lot of predictions about the likely meaning of a text and outputting highly probably-appropriate responses. Some AI is audio analysis, for speech recognition and speech generation (like Amazon Alexa). Some AI is to drive cars - which is a many-layer-algo environment for image analysis and math about mass/velocity/trajectory of moving objects. Very hard to do. It's important to know that the more you train AI, the better it will be. That's also why it's a long way to go before we have to be afraid of it. We're still training baby AI algorithms, in the grand scheme of things. Consider that a human baby can recognize a face - and even which one is its mother's - immediately upon exiting the womb. That is with ZERO face detection/recognition training. There's a lot we don't know about truly artificial intelligence. Armed with this basic understanding of AI, you can collaborate with data scientists to uncover opportunities for the application of machine learning to large datasets in your possession. If you have specific questions about applying AI to your business, don't waste time googling stuff. Let's chat.

I would create an account with Manageflitter and/or Followerwonk - both integrate with Twitter. I know that many people feel negatively about Twitter but here's why it's great, especially for your quesiton: There are no privacy settings. Facebook and LinkedIn have tons of walls you run into while cruising around looking for potential buyers. And the other social networks are not conducive to time-effective/cost-effective networking for other reasons. With these tools (Manageflitter and Followerwonk), you can search the bios of Twitter accounts for DPC, TPA, and any other relevant keyword. You can search within your location, or throughout Twitter as a whole. From there, the tools allow you to classify (List) or interact (Follow) them. After that, I'd encourage you to engage them subtly, by favoriting and retweeting a few of their Tweets over several days. Then begin a conversation with them, ideally about a mutual interest or some random thing they specify in their bio. After a back-and-forth tweet or two, bring up your services tactfully. Ask them honestly if they need what you are offering. If they don't, maybe they know someone who is. At the very least, you'll have a relevant networking partner/friend! Another very valuable use of your time would be to post in LinkedIn groups. They are more professional than Facebook groups, and it's common for people to post jobs and other networking opportunities there, as well as pitch their own services. (See the lengthy URL at the bottom of this answer for an example). Hope that helps - I'd love to talk more; there are several other ways you could go about connecting with DPC's and TPA's online. Thanks, Evan http://www.linkedin.com/groups?gid=1783783&trk=vsrp_groups_res_name&trkInfo=VSRPsearchId%3A1618903721380231777057%2CVSRPtargetId%3A1783783%2CVSRPcmpt%3Aprimary

Good question - digital B2B is definitely a particular challenge. One factor that determines the approach is the nature of your potential buyers' online habits. For example, if a significant percentage of your potential buyers use X social network, it makes sense to develop strategic marketing on X social network. This means the first step in the process is defining your digital target market. You may have a good idea what your target market is offline, but you've got to hop inside their heads when they get online, either on a desktop or mobile. It's true that there are myriad other ways to find your target market online beyond social media, but the value of social media lies in how most users declare who they are (and what their buying habits are) on their social networks (bio keywords, friends, hashtags, and other content they interact with). In order to define your target market online, I recommend three different strategies: 1. Manageflitter (manageflitter.com) and Followerwonk. These tools are for Twitter only, but that's ok because Twitter is amazing. ;) Using the "Bio keyword search" or "account search", type in words your potential buyers might use to describe themselves. (You can also narrow it by location, which sounds valuable to your South Africa specification). For example, your buyers might place "CEO" or "Owner" in their bio. (I have no idea, just giving an example). You will be able to view, classify, and interact with all the accounts on Twitter who match your specifications. 2. The other tool that aids greatly in target market definition online is Facebook Graph Search. It is a newer function of Facebook. Here's how you can take advantage of it: a. Find a very popular Facebook Page of a competitor or industry partner (or your own Facebook Page, if it has 500+ Likes). Let's say the page is named "Industry Partner" b. Type in the search bar: "Magazines people who like Industry Partner like" or "Websites people who like Industry Partner like" or even "Restaurants people who like Industry Partner like". With a large enough data pool, Facebook Graph Search provides an incredible amount of marketing insight: where your potential buyers eat, what they read, what websites they visit. Repeat this process on as many relevant pages as possible. If you find 3 websites they tend to like (ones that keep popping up), consider advertising via Google Adsense on those sites. Reach out to the admins of the sites (you may be able to find them by searching the site name on Twitter via Manageflitter or Followerwonk) and see if they are open to advertising or partnership opportunities -- perhaps a guest blog about how YOU are singlehandedly revolutionizing the industry. There are more ways to accomplish what you are looking for, but I hope these help. I guarantee they're highly valuable for how much time they take. Let me know if you would like more information. Thanks, Evan

It depends on what you're looking to monetize it with. One recommendation is affiliate marketing, because it happens under the table, in a sense. For example, get the plugin provided at the bottom of this answer. Browse amazon for products particular to your audience. If you're running a Facebook page and Twitter account for music-lovers, go to Amazon mp3s and get affiliate links for popular albums. Post an engaging status/tweet ("You have to check these guys out:") followed by the affiliate link. You get money for each purchase. Another option is fiverr.com (and other websites like it - microcommerce sites) where you can sell services for $5. It is common for people to buy tweets/posts from people with large social network accounts. For example: "I will Tweet your message to my 50,000 music-loving fans for $5". There are many other ways to monetize, including partnerships with sites trying to promote content that your fans will enjoy, but these are two of the simplest. They don't require anyone else for you to get started. Hope that helps! I'd love to talk more. Thanks, Evan https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CCwQFjAA&url=https%3A%2F%2Fchrome.google.com%2Fwebstore%2Fdetail%2Famazon-affliate-link%2Fpngekjpmhnigmkdojiplphdjpgbknpik%3Fhl%3Den-US&ei=06JEUr-UN6WdigKT64HQAw&usg=AFQjCNHbC7-bYE21Z3yccgWj48dcCWVPAg&sig2=y7u7v_1GqqkekkNArcF1UQ&bvm=bv.53217764,d.cGE&cad=rja