Warum Unternehmen bei der Einführung von KI auf Hindernisse stoßen

Das menschliche Gehirn ist eine wunderbare Maschine, die Wissenschaftler, Philosophen und Mathematiker seit Jahrhunderten zu emulieren versuchen. Wenn ein Mensch…

Das menschliche Gehirn ist eine wunderbare Maschine, die Wissenschaftler, Philosophen und Mathematiker seit Jahrhunderten zu emulieren versuchen. Wenn ein Mensch das kann – mit seinen emotionalen Schwächen und so weiter – könnte eine Maschine es dann nicht besser machen? Wenn das Geld, das in Startups für künstliche Intelligenz (KI) fließt, ein Hinweis darauf ist, dann lautet die Antwort ja.

Gartner hat KI zum Top-Trend für 2018 erklärt. Und Sie können kaum eine Veranstaltung besuchen, auf der KI nicht ganz oben auf der Liste steht( auch aufunserer Activate-Konferenz ). Wenn Sie dann noch Machine Learning (ML), Deep Learning (DL) und Natural Language Processing (NLP) hinzufügen, können Sie davon ausgehen, dass KI und ihre Untergruppen in aller Munde sind.

Doch trotz ihrer Allgegenwärtigkeit tun sich Unternehmen immer noch schwer, KI zu implementieren. Laut Adobe erwarten zwar 31 % der Unternehmen, dass sie KI im kommenden Jahr einführen werden, aber nur 15 % setzen sie heute ein. Laut Gartner werden bis 2020 50 % der Unternehmen nicht über ausreichende KI- und Datenkenntnisse verfügen, um einen geschäftlichen Nutzen zu erzielen.

Wenn das Versprechen so phänomenal ist, warum ist KI dann so ein Bürger zweiter Klasse? Wir haben vier KI-Experten gebeten, uns ihre Gedanken darüber mitzuteilen, was die Unternehmen zurückhält. Natürlich sind Daten und veraltete Infrastrukturen zwei der Herausforderungen, aber überraschenderweise auch die Talente. Denn obwohl KI angeblich den Menschen ersetzen soll, ist der Mensch immer noch ein wichtiger Teil der Gleichung.

Daten, ROI & Skalierbarkeit

Ich sehe tatsächlich, dass KI in kleineren Technologieunternehmen recht häufig eingesetzt wird. Sie ist in der Regel Teil des Kernstapels, denn die Gründer dieser Unternehmen sind in der Regel KI-Experten oder KI-Experten, die sich mit Fachexperten zusammenschließen. Ich habe immer wieder festgestellt, dass es etablierteren Unternehmen sehr viel schwerer fällt, KI zu übernehmen – insbesondere KI im Zusammenhang mit intelligenter Suche, maschinellem Lernen und der Verarbeitung natürlicher Sprache. Ein paar Gründe, warum ich denke, dass die Einführung langsam ist:

1. Fehlen einer guten internen Infrastruktur
Die Grundlage für die Entwicklung guter ML- und NLP-Modelle sind DATEN. Ganz gleich, ob es sich um beschriftete oder unbeschriftete Daten oder um Suchprotokolle handelt, Unternehmen müssen ohne weiteres über einen guten Datenspeicher verfügen, damit Datenwissenschaftler ihn untersuchen und Modelle erstellen können. Die Erstellung eines gut zugänglichen Datenspeichers ist eine große Investition und erfordert viel Zeit für Data Engineering.

Ein Datenspeicher ist ein Teil der Geschichte, eine Strategie für die Modellbereitstellung ein anderer. Wie kommen Sie von den Rohdaten in Ihrem Datenspeicher dazu, Vorhersagen über Betrugsaktivitäten auf Ihrer Website zu treffen? Dies ist eine weitere Investition, bei der Unternehmen sicherstellen müssen, dass es einen klaren technischen Weg gibt, um Ihre Modelle vom Prototyp zur Produktion zu bringen.

2. Verwirrung auf Managementebene über die Anwendbarkeit von KI
Was KI oder maschinelles Lernen WIRKLICH leisten können, ist für die meisten Produktmanager und Entscheidungsträger eine Art Blackbox. Wenn man nicht tagtäglich damit arbeitet, können die Konzepte einschüchternd wirken und es ist nicht sofort klar, wie diese Technologien helfen.

This causes a general confusion as to where AI and machine learning should be used to get the best ROI on dollars invested. I’ve seen countless hours wasted on solving problems with machine learning when all it really needed was several if-else rules.

3. The hype factor
Let’s talk data-related hypes. We have seen everything from big data to data science to machine learning. We are currently in the AI and deep learning hype phase. There will always be hyped up technologies and some companies tend to get really caught up in it. They try to fit their data and automation problems into the mold of the current hype. That just doesn’t work.

You could potentially run into scalability issues due to the complexity of the new technology or the technology limits you in such a way that it works only on a handful of use cases. These are all based on true events.

Kavita Ganesan is a Senior Data Scientist at Github and holds a Ph.D in Text Mining, Analytics and Search. She has over a decade of experience in building scalable Machine Learning and NLP models for various companies she has worked for including eBay, 3M and GitHub. In 2017, Kavita led the launch of the first production scale NLP and Machine Learning pipeline at GitHub with the release of GitHub topics touching millions of repositories.

AI/Machine Learning Talent & Transparency

Artificial Intelligence has extensive, conceivable applicability. However, we’re still in the initial stages in terms of the adoption of these technologies, so there’s a long way to go.

AI has potential application across various sectors, e.g, healthcare, retail, semiconductors, etc.
However, when evaluating use of AI, business leaders have to keep in mind that this is still a very fast-evolving set of techniques and technologies.

There are confines that are purely technical. Typical questions when moving from, for example, a historically database-based system are:
1. Can we actually explain what the new AI/ML algorithm is doing?
2. Can we interpret why it’s making the selections and the consequences and forecasts that it’s making?
3. If the results are not much different, then why should I change?

And there are some real-world limitations as well. A lot of data is needed for the AI/ML algorithms to train and the data needs to be labeled. But is the data actually available? And is it labeled?

If data exists, there are still questions about:
• How clear are the algorithms?
• Is there any bias in the data?
• Is there any prejudice in the way the data was collected?

Though we have this idea of machine learning, we first have to train humans to collect and train the data and algorithms. Now companies have to find this talent pool of people who are able to do it.

And, companies have to keep an open mind. Like every new technology adaptation, things will eventually be easier and more evolved. Most companies have a lot of data, which is mostly wasted, so it’s very important to invest in prepping and cleansing the data so it can be made useful.

If you cannot hire new talent, train the current talent. The concept of AI is very human understandable. We do learn by observing the patterns in our surroundings and AI is training machines to learn from the data we feed them and reach conclusions in a way similar to a human. That means it can take examples and learn from them to increase the accurateness of its future conclusions.

I think business leaders should just start to understand the technology and what’s possible. Think of how AI works as the same way you learn what a dog looks like by looking at photographs of labradors, poodles, and pit bulls. Then later, when you’re shown a picture of a doberman, being able to identify that as another breed of dog.

Now try and understand what the implications of that type of learning could have on your organization. Consider real estate: by having the machine look at past house prices and house descriptions—it can predict the price of any house if it has the house data.

The machine can reach a new, accurate, conclusion based on what it has learned in the past.

AI/ML is widely applicable. So, understand where it can help you get value.

Anupama Joshi is the Senior Engineering Manager at Reddit, managing the search and discovery efforts from ingestion to results and infra to ranking. Expert in managing cross-disciplined teams, her organization is focused on the infrastructure and development of core ranking algorithms and ranking signals to optimize the quality of search results.

Legacy Code & Infrastructure

I think AI is extremely important for most companies right now to automate existing processes, create new innovative ones, and increase the efficiency of existing processes across the board. While the tools and talent for incorporating AI in business exists, there are some hurdles along the way.

A lot of companies have legacy code and infrastructure that are not easy to build AI into, and require a lot of investment from the business. AI algorithms are often built on top of a data layer and having easy access to reliable, structured data can be difficult. There are some difficulties around finding the right talent, picking the right tools and seeing and communicating results from AI in a reasonable amount of time across the organizations.

I think most of the difficulties around implementing AI in business stems from the difference between AI development vs classic software development.

Successful AI implementation and integration require a shift in mindset, strategy and clear communications between business and the machine learning or data science teams. In most cases, integrating AI in an organization requires an investment from the business. This is due to AI’s nature.

AI is an iterative process and in most cases immediate results are not possible before building the right underlying infrastructure and rounds of iterations and refining the input data, features, modeling, and inference.

I usually advise the AI teams to focus on building the correct data layer, infrastructure, and the simplest model, and focus on iterations involving more advanced modeling and featurization techniques afterwards. This requires clear communications with the organization and setting realistic goals and timelines.

I think organizations should define long-terms goals and vision around AI and shift away from quick wins or “fixes” mindset. Organizations can create clear goals around AI, invest in this iterative process and enable their data scientists and engineers to build what is necessary for successful implementation of AI!

Kamelia Aryafar, Ph.D., is the Chief Algorithms Officer at Overstock.com, leading the company’s ML, data science, data engineering and analytics functions across the marketing, customer, sourcing, and website verticals. Since joining Overstock.com in 2017, her teams have integrated ML and AI algorithms across various product teams, including personalization, pricing, ranking, search, recommender systems, marketing, CRM, advertising technologies, email, sourcing, and supply chain.

Human Talent & Vision Are Key

I’m one who thrives on envisioning and architecting how data, artificial intelligence, and technology can make our world a better, easier place to live. The reality is that AI systems are really hard to implement. AI is still in its infancy. Just because an AI system won against a human at a game, it doesn’t mean that it can be used in your business to drive immediate outcomes.

Building AI systems for an organization needs vision (a leader with a unique combination of business and technical strength), expertise (talent), and data (domain specific and in massive quantity)—none of these are easily available in most organizations.

Unless an organization is ready to make the investments necessary to get the above three factors in place, they will find it very hard to succeed in building and implementing AI systems.

Beena Ammanath, is the founder and CEO of nonprofit, Humans For AI Inc. and is an award-winning senior digital transformation leader with extensive global experience in Artificial Intelligence, big data, and IoT. Her knowledge spans across e-commerce, financial, marketing, telecom, retail, software products, services and industrial domains with companies such as Hewlett Packard Enterprise, GE, Thomson Reuters, British Telecom, Bank of America, e*trade and a number of Silicon Valley startups.

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